Recent climate change has brought new patterns of extreme events in terms of both drought and heavy rainfall to the drought‐prone African Sahel. The effects of these recent extreme events on the performance of the Sahel farming systems are still weakly investigated. This study aims at assessing effects of droughts versus floods on crop yield levels and losses, focusing on the so‐called recovery period, particularly 2001–2020. A newly developed productivity‐drought condition index (PDCI) is utilized to assess agricultural productivity as related to drought or flood in a highly vulnerable region, that is, the Sudanese Sahel. Four farming systems, namely traditional rainfed, mechanized rainfed, gravity irrigated and spate irrigated systems, with sorghum and millet as staple food crops, are considered. The PDCI is defined as a function of the integrated normalized difference vegetation index (iNDVI) over the growing season. To address temporal and spatial variabilities, scaling of the PDCI is done in two dimensions: space and time. Crop statistics are used to derive yield losses. Our results show that both drought and flood episodes (seven and six episodes, respectively) can be captured using the PDCI. Drought remains the most relevant risk to Sahel's crop productivity. Some recent large‐scale floods led to yield loss. However, floods cause smaller risks to agricultural productivity compared to droughts. Floods may even result in enhanced crop yields. Based upon scaling in the time or space domain, ranking the severity of drought impacts on crop yield for individual years from 2001 to 2020 reveals least to slightly different results. Vulnerability to drought depends on the crop type and farming system. Drought effect on crop yield from the irrigated sector is clear on individual years but not as a general statistical relationship. The parameter ‘percentage area under drought’ explains around one‐third of the variation in the rainfed crop yield. The spate irrigation scheme, the gravity irrigated system and the rainfed farmlands experienced respectively 87%, 57% and 46% of area under drought on average. Irrigated systems produce much higher crop yields than rainfed systems. The mechanized system is more drought‐vulnerable than the traditional system. These results call for identifying agricultural management pathways that recognize the combined implications of both hydrological extremes for the region's food security.

Palm trees, referred to here as vegetation cover (VC), provide essential ecosystem services in an arid Oasis. However, because of socioeconomic transformation, the rapid urban expansion of major cities and villages at the expense of agricultural lands of the Al-Ahsa Oasis, Saudi Arabia, has placed enormous pressure on the palm-growing area and led to the loss of productive land. These challenges highlight the need for robust, integrative methods to assess their impact on the agroecosystem. Here, we analyze spatiotemporal fluctuations in vegetation cover and its effect on the agroecosystem to determine the potential influencing factors. Data from Landsat satellites, including TM (Thematic mapper of Landsat 5), ETM+ (Enhanced Thematic mapper plus of Landsat 7), and OIL (Landsat 8) and Sentinel-2A imageries were used for analysis, while GeoEye-1 satellite images as well as socioeconomic data were applied for result validation. Principal Component Analysis (PCA) was applied to extract pure endmembers, facilitating Spectral Mixture Analysis (SMA) for mapping vegetation and urban fractions. The spatiotemporal change patterns were analyzed using time- and space-oriented detection algorithms. Results indicated that vegetation fraction patterns differed significantly; pixels with high fraction values declined significantly from 1990 to 2020. The mean vegetation fraction value varied from 0.79 to 0.37. This indicates that a reduction in palm trees was quickly occurring at a decreasing rate of −14.24%. Results also suggest that vegetation fractions decreased significantly between 1990 and 2020, and this decrease had the greatest effect on the agroecosystem situation of the Oasis. We assessed urban sprawl, and our results indicated substantial variability in average urban fractions: 0.208%, 0.247%, 0.699%, and 0.807% in 1990, 2000, 2010, and 2020, respectively. Overall, the data revealed an association between changes in palm tree fractions and urban ones, supporting strategic vegetation and/or agricultural management to enhance the agroecosystem in an arid Oasis.

Water quality has become a major concern for public health, agriculture, and industry, necessitating reliable and continuous monitoring. Conventional monitoring methods are often time-consuming, rely on manual sampling, and involve complex equipment or procedures, making them unsuitable for real-time applications. This study presents an Internet of Things (IoT)-based system for real-time water quality monitoring using ESP32 hardware integrated with the ThingSpeak platform. The system enhances the accuracy of turbidity and pH measurements using advanced sensor calibration techniques. Nephelometric methods and glass electrodes are employed for turbidity detection and pH sensing, respectively, across various water types—including tap water, groundwater, wastewater, saline water, and treated water—to address issues such as environmental drift and measurement inaccuracies. The turbidity sensor was calibrated using a standard six-point method with formazin solutions (0–1064 NTU), whereas pH calibration utilized a three-point approach with NIST-traceable buffer solutions (pH 4, 7, and 10). The results indicate that turbidity measurement errors, initially ranging from 15.75% to 422%, were reduced to below 10% after calibration. Similarly, pH accuracy was significantly improved across all tested water matrices. The system enables real-time data visualization via ThingSpeak, and the implementation of multi-point calibration ensures high data reliability for continuous monitoring. Overall, this approach offers an accurate, efficient, and practical solution for real-time water quality management.

This chapter investigates the complex relationship between agricultural fertilization, sustainability, and climate change in Saudi Arabia, a region marked by extreme aridity and water scarcity. As the global population approaches 10 billion, the challenge of ensuring food security while safeguarding ecological systems intensifies. The analysis traces the historical trajectory of fertilization practices, highlighting the Kingdom’s transition from traditional oasis farming to modern, high-input agriculture. While this shift initially bolstered food production, it has also led to significant environmental degradation, including soil depletion, water pollution, and increased greenhouse gas emissions. The chapter emphasizes the urgent need for sustainable fertilization practices that prioritize innovative technologies and regulatory frameworks. It assesses current dependencies on fertilizers and explores sustainable alternatives such as precision agriculture, hydroponics, and organic farming. The findings underscore that while Saudi Arabia has achieved considerable food security, these intensive practices have environmental costs that cannot be overlooked. In response, the Kingdom is advancing strategic initiatives, including climate-resilient crops and efficient irrigation systems, supported by policies such as Vision 2030, which align economic diversification with sustainability goals. Ultimately, this chapter advocates for a multifaceted approach that integrates technological innovation, robust policy support, and farmer education, essential for developing a resilient agricultural sector capable of meeting food security demands while mitigating environmental impacts in a changing climate. Through case studies and comprehensive analyses, the chapter lays the groundwork for future research and action toward sustainable nutrient management in Saudi Arabia’s agricultural landscape.