Climate models project that many terrestrial ecosystems will become drier over the course of this century, leading to a drastic increase in the global extent of arid soils. In order to decrease the effects of climate change on global food security, it is crucial to understand the arid environment and the constraints associated with arid soils. Although the effects of aridity on aboveground organisms have been studied extensively, our understanding of how it affects soil processes and nutrient cycling is lacking. One of the primary agricultural constraints, particularly in arid locations, is water scarcity, due to which arid soils are characterized by sparse vegetation cover, low soil organic carbon, poor soil structure, reduced soil biodiversity, and a high rate of soil erosion via wind. Increased aridity will limit the availability of essential plant nutrients and crop growth, and subsequently pose serious threats to key ecological processes and services. The increasing rate of soil salinization is another major environmental hazard that further limits the agricultural potential of arid soils. These soil constraints can be ameliorated and the crop yields increased through case-specific optimization of irrigation and drainage management, enhancing the native beneficial soil microbes, and combinations of soil amendments, conditioners, and residue management. This review explores technologies to ameliorate soil constraints and increase yields to maintain crop output in arid soils.

Purpose: Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of global estimates for SOC using remote sensing (RS) and machine learning (ML) techniques. It showcases the historical growth and thematic evolution in SOC research, aiming to amplify the understanding of SOC estimation themes and provide scientific support for climate change adaptation and mitigation. Materials and Methods: Employing extensive literature database analysis, bibliometric network analysis, and clustering techniques, the study reviews 1,761 articles on SOC estimation using RS technologies and 490 articles on SOC employing both RS and ML technologies. Results and Discussion: The results indicate that satellite-based RS, particularly the Landsat series, is predominant for estimation of SOC and other associated studies, with North America, China, and Europe leading in evaluations with Africa is having low evaluations adopting RS technology. Trends in the research demonstrate an evolution from basic mapping to advanced topics such as carbon (C) sequestration, complex modeling, and big data utilization. Thematic clusters from co-occurrence analysis suggest the interplay between technology development, environmental surveys, soil properties, and climate dynamics. Conclusion: The study highlights the synergy between RS and ML, with advanced ML techniques proving to be critical for accurate SOC estimation. These findings are crucial for comprehensive ecosystem SOC estimation, informed environmental management and strategic decision-making.

Conservation agriculture (CA) is considered a sustainable practice with the potential to maintain or increase crop productivity and improve environmental quality and ecosystem services. It typically improves soil quality and water conservation; however, its effect on crop productivity is highly variable and dependent on local conditions/management. Crop residue retention plays a crucial role in CA and can help to improve overall soil health and ultimately crop productivity and sustainability. However, weed control, herbicide resistance, and weed shift under residue retained fields is a major challenge. Moreover, CA can increase water infiltration and reduce soil loss and runoff. This reduces the surface transport of nitrate and phosphorus from agricultural fields and the eutrophication of water bodies, although leaching of nitrate to groundwater can potentially increase. In addition, CA has been proposed as one of the components in climate-smart agriculture, owing to its reduced period to seed/plant next crop, reduced soil disturbance and low consumption of fossil fuels. Therefore, compared to the conventional intensive tillage, CA has a greater potential for soil C sequestration, favors higher soil biodiversity, lowers greenhouse gas emission, and can assist in mitigating climate change. However, not all experiments report a positive impact. The understanding and decoding the site-specific complexities of CA system is important and requires a multidisciplinary approach.