Ensuring global food security and achieving sustainable agricultural productivity remains one of the foremost challenges of the contemporary era. The increasing impacts of climate change and environmental stressors like drought, salinity, and heavy metal (HM) toxicity threaten crop productivity worldwide. Addressing these challenges demands the development of innovative technologies that can increase food production, reduce environmental impacts, and bolster the resilience of agroecosystems against climate variation. Nanotechnology, particularly the application of nanoparticles (NPs), represents an innovative approach to strengthen crop resilience and enhance the sustainability of agriculture. NPs have special physicochemical properties, including a high surface-area-to-volume ratio and the ability to penetrate plant tissues, which enhances nutrient uptake, stress resistance, and photosynthetic efficiency. This review paper explores how abiotic stressors impact crops and the role of NPs in bolstering crop resistance to these challenges. The main emphasis is on the potential of NPs potential to boost plant stress tolerance by triggering the plant defense mechanisms, improving growth under stress, and increasing agricultural yield. NPs have demonstrated potential in addressing key agricultural challenges, such as nutrient leaching, declining soil fertility, and reduced crop yield due to poor water management. However, applying NPs must consider regulatory and environmental concerns, including soil accumulation, toxicity to non-target organisms, and consumer perceptions of NP-enhanced products. To mitigate land and water impacts, NPs should be integrated with precision agriculture technologies, allowing targeted application of nano-fertilizers and nano-pesticides. Although further research is necessary to assess their advantages and address concerns, NPs present a promising and cost-effective approach for enhancing food security in the future.

The land use and land cover (LULC) changes and the implications of soil and irrigation water salinity have adverse effects on crop production and the ecosystems of arid and semiarid regions. In this study, an attempt has been made to analyze and monitor the LULC changes using multitemporal Landsat data for years 1986, 1998, 2007, and 2016 in Al-Ahsa Oasis, Saudi Arabia. In addition, efforts were made to measure the spatial distribution of soil and irrigation water salinity along the oasis. The supervised maximum likelihood classification method was applied to classify the individual images independently. Moreover, soil samples were collected at surface soil depth from the selected LULC types, namely, date palm, croplands, and bare land. Also, groundwater samples were collected from bore wells located in agricultural farms. The spatial distribution of the soil salinity (Ec_e) and irrigation water salinity (EC_iw) was classified based on the Food and Agriculture Organization guidelines. The results showed that significant changes in LULC patterns have occurred during 1986–2016 in the study area. The EC_ewas found higher in date palm compared with cropland and bare land. However, the spatial distribution of the EC_iwover the oasis indicated that 94% of irrigation water ranged between moderate and severe salinity risk. The study concludes that salinity management practices need to be developed in the study area aiming to sustain crop yields, improve soil properties, and minimize the environmental impacts of LULC changes on the ecosystem of Al-Ahsa Oasis.

This research utilized the bagging machine learning algorithm along with the Thornthwaite moisture index (TMI) to enhance the understanding of climate variability and change, with the objective of identifying the most efficient climate service pathways in Sub-Saharan Africa (SSA). Monthly datasets at a 0.5° resolution (1960–2020) were collected and analyzed using R 4.2.2 software and spreadsheets. The results indicate significant changes in climatic conditions in Sudan, with aridity escalation at a rate of 0.37% per year. The bagging algorithm illustrated that actual water use was mainly influenced by rainfall and runoff management, showing an inverse relationship with increasing air temperatures. Consequently, sustainable strategies focusing on runoff and temperature control, such as rainwater harvesting, agroforestry and plant breeding were identified as the most effective climate services to mitigate and adapt to climate variability in SSA. The findings suggest that runoff management (e.g., rainwater harvesting) could potentially offset up to 22% of the adverse impacts of climate variability, while temperature control strategies (e.g., agroforestry) could account for the remaining 78%. Without these interventions, climate variability will continue to pose serious challenges to food security, livelihood generations, and regional stability. The research calls for further in-depth studies on the attributions of climate variability using finer datasets.

Evapotranspiration (ET) estimation is crucial for sustainable water resource management in arid and semi-arid regions, particularly in the Arab world, where water scarcity remains a significant challenge. The objectives of this study were to map dominant ET estimation techniques and their geographic distribution, demonstrate fusion-based ET estimation under data-scarce conditions, and examine their alignment with climate change and food security priorities. The study reviewed 1279 ET-related articles indexed in the Web of Science, highlighting methodological trends, regional disparities, and the emergence of data-driven techniques. The results showed that traditional methods—primarily the Penman-Monteith model—dominate nearly 70% of the literature. In contrast, machine learning (ML), remote sensing (RS), and artificial intelligence (AI) collectively account for approximately 30%, with hybrid fusion frameworks appearing in only 2% of studies. ML applications are concentrated in Morocco, Egypt, and Iraq, while 50% of Arab countries lack any ML or AI-based research on energy transition (ET). Complementing the bibliometric analysis, this study demonstrates the practical potential of ML-based ET fusion using Landsat and the FAO Water Productivity (WaPOR) data within Saudi Arabia. A random forest model outperformed traditional averaging, reducing the mean absolute error (MAE) to 215.08 mm/year and the root mean square error (RMSE) to 531.34 mm/year, with a Pearson correlation coefficient of 0.86. The findings advocate for greater support and regional collaboration to advance ET monitoring and integrate ML-based modelling into climate resilience frameworks.