Supplementation of the growing substrate by nitrogenous additives has been known to improve the production of oyster mushroom (Pleurotus ostreatus (Jacq. ex Fr.) P. Kumm. (1871)). However, the application of nano-additives has not been reported in such cultivation yet. The study investigated the effect of nano-urea added in two different doses (3 g and 5 g per kg substrate), once (at spawning or after first flush) or twice (at spawning and after first flush) to the growing substrate consisting of wheat straw and spent oyster substrate (1:1, w/w). Results showed that the application of nano-urea once has induced the highest number of mushroom flushes (four flushes) despite the dose applied. Contrarily to early findings, where high doses of nitrogen have caused inhibition of mushroom growth and production, nano-urea application has had better effects when applied twice. With 5 g/kg, it induced the shortest period between the first and the third flush (15 days). With 3 g/kg, it resulted in the highest biological and economic yields at the third flush (332.7 g/bag and 283.1 g/bag respectively), in total (973.4 g/bag and 854.0 g/bag respectively), the highest biological efficiency (109.6%), and pileus diameter/stipe length ratio (2.8). Experimental findings of the current study may be potentially applied at commercial scale.

Climate change poses a challenge to the security and long-term viability of the global food supply chain. Climate unpredictability and extreme weather events have significant impacts on Saudi Arabia’s vulnerable food system, which is already under stress. The Kingdom of Saudi Arabia faces distinct challenges in comparison to other dry locations across the world. Here, the per capita water demand is high, the population is growing, the water resources are extremely limited, and there is little information on the existing groundwater supplies. Consequently, it is anticipated that there will be formidable obstacles in the future. In order to make data-driven decisions, policymakers should be aware of causal links. The complex concerns pertaining to the Saudi Arabian food system were analyzed and rationally explained in the current study. A causality analysis examined different driving factors, including temperature, greenhouse gas (GHG) emission, population, and gross domestic product (GDP) that cause vulnerabilities in the country’s food system. The results of the long-run causality test show that GDP has a positive causal relationship with the demand for food, which implies that the demand for food will increase in the long run with an increase in GDP. The result also shows that Saudi Arabia’s GDP and population growth are contributing to the increase in their total GHG emissions. Although the Kingdom has made some efforts to combat climate change, there are still plenty of opportunities for it to implement some of the greatest strategies to guarantee the nation’s food security. This study also highlights the development of appropriate policy approaches to diversify its import sources to ensure future food security.

The capacity of Pleurotus ostreatus to degrade the lignocellulose of spent coffee grounds (SCG) and its resultant nutrient composition is not yet studied. The study tested four SCG/wheat straw mixtures, containing 0, 33, 67, or 100% SCG. Initial lignin content was higher, and lignin loss was lower in mixtures containing SCG, compared to control revealing lower substrate degradation. Biological yield was negatively correlated with initial lignin content, and positively with the lignin loss from substrates. The nutritional value of mushrooms was improved by a general increase in carbohydrates, a decrease in sodium content with 33% and 67% SCG, and lower fat content with 33% SCG. Polyunsaturated fatty acids of mushrooms (PUFA) increased with 67% SCG; while saturated ones (SFA) increased with 33 and 67% SCG. All mushrooms had a close PUFA/SFA, ranging between 7.4 and 8.1. The study suggests using SCG as a nutritional supplement to the commercial wheat straw. Graphical abstract

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R²) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO₂e) and mean absolute percentage error (0.29 GtCO₂e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO₂ equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.

Bioethanol production offers promise in mitigating environmental impacts from ethanol consumption despite water scarcity. This study endeavors to evaluate the nuanced influence of different deheading times (45 days before harvest, 21 days before harvest, and no deheading) along with varying water regimes on select sweet sorghum cultivars (Honey, Willy, MN1500, and Atlas), focusing on yield traits, theoretical ethanol production, and water productivity. Findings underscore the substantial impact of cultivation practices on bioethanol yield. A water deficit ranging from 30% to 70% resulted in a discernible reduction in stalk yields of 17.86% to 18.54% and in sugar yields of 0.2 to 0.31 Mg ha⁻¹, accompanied by a corresponding decline in theoretical ethanol yield of 120.9 to 180.9 L ha⁻¹. Additionally, notable enhancements in Brix and sugar content of 16.32% to 18.42% and 16.81% to 19.03%, respectively, were observed across both seasons. Of particular significance, the Honey variety, subjected to a 30% water deficit and deheading at 21 days before harvest, demonstrated exceptional growth and yield characteristics. These empirical insights furnish valuable guidance for optimizing sweet sorghum cultivation practices, thereby augmenting sustainable bioethanol production and propelling forward the frontier of renewable energy technologies towards a more environmentally sustainable future.

In pursuance of the Saudi vision 2030, the Al-Hasa municipality has been allocating a total of US$ 60.1 million since 2018 to the implementation of a modern Municipal Solid Wastes (MSW) management system. In addition to the improved old components, the system involves six...