Water is essential for sustaining life and required for carrying out basic daily activities. Even though water covers the vast majority of the earth’s surface, the availability of fresh water, which is necessary to maintain human activities, is limited, making it a scarce resource. Climate change, overexploitation of groundwater, and population growth are all putting significant pressure on natural water sources, which pose a serious threat to various sectors of society, especially in agriculture. Future projections of freshwater availability indicate agriculture production will suffer a significant shock globally, including in India, leading to a threat to food security and sustainability. To ensure the sustainability of this vital resource, it is crucial to use water sensibly. Moreover, it is essential to adopt certain strategies to manage agricultural water use effectively. This includes adopting various water-efficient techniques such as ‘micro-irrigation’, ‘irrigation scheduling’, ‘conservation agriculture’, ‘crop switching’ and so on. In this review, firstly, we discuss water scarcity and its types, causes, crisis for water shortages and hindrance to sustainable development from a global perspective emphasizing the Indian scenario as a developing nation. Secondly, we elaborated our discussion on water scarcity in agriculture including the impacts of water scarcity on agricultural production and its connection to climate change, population growth, and overexploitation of natural resources globally focusing on the Indian scenario. In addition, innovative water management practices and adaptation strategies to manage agricultural water use, constraints, and the need for further research are also covered. It is anticipated that this review will benefit researchers and policymakers by providing useful information on the impacts of water limitation and adoption strategies.

The accelerating pace of urbanization, coupled with the intensifying impacts of climate change, poses unprecedented challenges to both the environment and human well-being. In this review, we delved into the intricate interaction between climate change and urbanization and the various effects they have on the environment and human well-being, shedding light on the emergent urban climate crisis. Urban areas serve as epicenters for diverse socio-economic activities, yet they also contribute significantly to global greenhouse gas emissions and environmental degradation. Through an interdisciplinary lens, we explored the root causes of the urban climate crisis, examining how rapid urbanization exacerbates climate change and vice versa. By synthesizing current research and case studies, we elucidate the various environmental and social ramifications of this nexus, ranging from urban heat island effects to heightened vulnerability to extreme weather events. Furthermore, we delve into the unequal distribution of climate risks within urban populations, highlighting the disproportionate burden borne by marginalized communities. Finally, the chapter presents strategies and interventions for mitigating and adapting to the urban climate crisis, emphasizing the imperative of holistic and equitable approaches that prioritize both environmental sustainability and human well-being. Overall, this review calls for concerted efforts to unravel the complexities of the urban climate crisis and forge a path toward resilient, sustainable, and equitable urban futures.

To evaluate the essential role of energy efficiency in achieving net-zero emissions and combating climate change, this comprehensive review was conducted based on a thorough analysis of academic literature, diverse case studies, and industry reports, focusing on key energy efficiency sectors and technological advancements. It begins by outlining global efforts, including the Paris Agreement and the Sustainable Development Goals, which have driven initiatives to enhance energy efficiency across various sectors. The review examines key technological innovations, such as advanced manufacturing, energy-efficient building designs, transportation electrification, and smart grid integration contributing to energy savings and decarbonization. It also discusses policy frameworks, regulatory incentives, and financial mechanisms that encourage energy efficiency. The study provides the detailed outcomes of the different strategies and initiatives, showcasing successful case studies from different regions in transportation, urban planning, power generation, and industry. It illustrates how countries have effectively implemented energy efficiency measures to reduce emissions and meet climate goals. Additionally, the review explores the potential of emerging technologies, including artificial intelligence, big data, and blockchain, to optimize energy management, enhance grid flexibility, and promote further efficiencies. Despite these advancements, the review identifies several key barriers - financial, technological, and behavioral challenges that impede the widespread adoption of energy-efficient practices. Finally, it offers strategies to overcome these obstacles, emphasizing the need for tailored policies, innovative business models, and greater consumer engagement. This review underscores the importance of energy efficiency in achieving a sustainable, net-zero future by synthesizing successful case studies and highlighting emerging solutions.

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R² = 0.89; RMSE = 34.8 g/m² for the MS and R² = 0.82; RMSE = 26.4 g/m² for the HS site) and grain yield (R² = 0.88; RMSE = 11.8 g/m² for the MS and R² = 0.74; RMSE = 16.1 g/m² for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.