Drought stress is an important challenge to global food security and agricultural output, and dramatic and rapid climate change has made the problem worse, causing unexpected impacts on the growth, development, and yield of different plants. Understanding the biochemical, ecological, and physiological reactions to these pressures is essential for improved management. Carbon materials’ impacts on plants subjected to different stresses are still poorly studied. Thus, this study was carried out investigate the feasibility of applying carbon nanotubes (CNTs) (0, 20, and 40 mg/L) as a foliar treatment for mitigating the effect of water stress (100%, 75%, and 50% irrigation water, IW) on peanut plants growing in sandy soil through assessments of growth and productivity and some physiological and biochemical measurements. Exposure of peanuts to decreased irrigation water led to significant decreases in growth, yield, photosynthetic pigments, indole acetic acid (IAA), and some nutritional components in peanut seeds, but increased levels of osmolytes such as total soluble carbohydrates (TSS) and proline, in addition to free amino acids and phenolics. However, foliar spraying with CNTs could ameliorate the impacts of decreased irrigation water on growth and production via enhancing the studied physiological parameters, such as photosynthetic pigments, IAA, osmolytes, and phenolics. Furthermore, the application of carbon nanotubes improved the nutrient contents, as expressed by the oil yield, protein yield, total carbohydrates, antioxidant activities (DPPH), B-carotene, lycopene, and flavonoids in peanut seeds, either under normal or water stress conditions. The higher level of CNTs (40 mg/L) was more effective than the lower one (20 mg/L) at increasing the above-mentioned parameters. In conclusion, foliar treatment with carbon nanotubes has the ability to enhance peanut drought tolerance and increase its growth and productivity under sandy soil conditions.

The nitrogen use efficiency (NUE) is the ratio of the N uptake by the plant to the total amount of applied N fertilizer. The optimization of NUE is essential for sustainable agriculture, especially in the breeding programs to improve the yield of barley (Hordeum vulgare L.). We investigated the NUE variation in 32 barley genotypes and to examine the response relationship between phenotypic traits and N level to determine the traits essential for the selection of breeding programs to optimize the NUE. The NUE was investigated in 32 barley genotypes for 2 yr under field conditions using three N levels (60, 120, and 180 kg N ha⁻¹). Growth, physiological and agronomic traits, and N efficiency‐related parameters were determined. The parameters investigated were significantly affected by N application and its interactions with traits. Plant height, biological yield, spike length, and grain number (GN) were increased at higher N levels, but the 1,000‐grain weight decreased. The lowest grain yield was 2,000.7 kg ha⁻¹ at 60 kg N ha⁻¹, and the highest was 8,607.9 kg ha⁻¹ at 120 kg N ha⁻¹. An increase in the N rate from 60 to 120 kg N ha⁻¹ was associated with a higher total chlorophyll content (CHL_SPAD) (from 19.04 to 23.13), but no significant change in CHL was seen at 180 kg N ha⁻¹. The agronomic nitrogen use efficiency (ANUE), fertilizer N recovery efficiency and the nitrogen utilization efficiency were both reduced with an increase of the N application rate. A path analysis revealed that GN was the most essential trait for NUE breeding program selection due to its high direct effect (0.72) on ANUE. A linear discriminant analysis showed that CHL at 60 d after sowing was the best trait for distinguishing low from high N levels.

Refine current agricultural practices considering environmental changes are crucial for finding tolerant rice varieties that can meet the demands of human consumption. To this end, stability analysis assesses a crop genotype’s ability to adapt to various conditions. Therefore, the objective of this study was to (1) examine the interaction between rice genotypes and environmental conditions; (2) evaluate the stability of twelve rice genotypes using various stability methods; (3) identify representative environments for multi-environment testing; and (4) determine superior genotypes for specific environments. The evaluated rice cultivars were Sakha 101, Sakha 104, Sakha 105, Sakha 106, Sakha 107, Sakha 108, Giza 177, Giza 178, Giza 179, Giza 182, Egyptian Yasmine, and Sakha super 300. The experiment followed a strip-plot design, with three replications. The findings revealed significant differences among the rice varieties across various environments for the majority of the assessed characteristics. The joint regression analysis of variance demonstrated highly significant differences among rice cultivars for all the studied traits in terms of genotype-by-environment interaction (G × E). The statistical significance of the interaction between genetic and environmental factors was evident for all variables demonstrating heritable variation among the rice cultivars, specifically Sakha 108, Sakha 104, Giza 177, and Giza 178, concerning grain yield per feddan. These rice cultivars exhibited stability parameters that were not significantly different from unity for the regression coefficient (b_i) and from zero for the deviations from regression (S²d_i) for those traits. Overall, stability criteria are essential for ensuring reliable rice production, meeting human consumption, advancing genetic improvement, and promoting environmental sustainability in agriculture.

This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF’s most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress R_p0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R² (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R² = 0.90 ± 0.028). Ra values greater than 1.1 and R_p0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9–35 for viscosity and 105–190 for R_p0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9–35, and the Ra was 0.95–1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the R_p0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

Wheat is one of the most important cereal crops in Egypt and all over the world. Its productivity is adversely affected by drought due to deficient irrigation to provide nutrients required for plant growth. In a field experiment, silicon foliar applications at concentrations of 0, 200, and 400 mg L⁻¹ were performed at different irrigation rates ranging from 1000 to 4000 m³ ha⁻¹ to assess water irrigation productivity and wheat crop yield in a calcareous soil under arid climate conditions. Increased irrigation rates led to a significant increase in soil nutrient dynamics, as well as in the number and weight of grains per spike, leaf area index, grain yield, straw yield, and biological yield, with the exception of the weight of 1000 grains. Spraying with sodium silicate had a significant impact on grain yield and harvest index but did not significantly impact the other traits. Furthermore, the interaction between irrigation and silicate application rates showed significance only for grain yield, the number of spikes/m², and the harvest index. Applying three times irrigation could produce the highest nutrient retention, wheat yield, and water irrigation productivity. No significance was observed between 3000 m³ ha⁻¹ and 4000 m³ ha⁻¹ irrigation, indicating a saving of 25% of applied irrigation water. It can be concluded that applying irrigation at 3000 m³ ha⁻¹ could be a supplemental irrigation strategy. High wheat grain yield can be achieved under deficit irrigation (3000 m³ ha⁻¹) on the northwestern coast of Egypt with an arid climate by spraying crops with sodium silicate at a rate of 400 mg L⁻¹.