Biochar is gaining significant attention due to its potential for carbon (C) sequestration, improvement of soil health, fertility enhancement, and crop productivity and quality. In this review, we discuss the most common available techniques for biochar production, the main physiochemical properties of biochar, and its effects on soil health, including physical, chemical, and biological parameters of soil quality and fertility, nutrient leaching, salt stress, and crop productivity and quality. In addition, the impacts of biochar addition on salt-affected and heavy metal contaminated soils were also reviewed. An ample body of literature supports the idea that soil amended with biochar has a high potential to increase crop productivity due to the concomitant improvement in soil structure, high nutrient use efficiency (NUE), aeration, porosity, and water-holding capacity (WHC), among other soil amendments. However, the increases in crop productivity in biochar-amended soils are most frequently reported in the coarse-textured and sandy soils compared with the fine-textured and fertile soils. Biochar has a significant effect on soil microbial community composition and abundance. The negative impacts that salt-affected and heavy metal polluted soils have on plant growth and yield and on components of soil quality such as soil aggregation and stability can be ameliorated by the application of biochar. Moreover, most of the positive impacts of biochar application have been observed when biochar was applied with other organic and inorganic amendments and fertilizers. Biochar addition to the soil can decrease the nitrogen (N) leaching and volatilization as well as increase NUE. However, some potential negative effects of biochar on microbial biomass and activity have been reported. There is also evidence that biochar addition can sorb and retain pesticides for long periods of time, which may result in a high weed infestation and control cost.

The APSIM-Wheat and AQUACROP models were calibrated for the Sakha 95 cultivar using phenological data, grain and biomass yield, and genetic parameters based on field observation. Various treatments of planting dates, irrigation, and fertilization were applied over the two successive winter growing seasons of 2019/2020 and 2020/2021. Both models simulated anthesis, maturity dates, grain yield, and aboveground biomass accurately with high performances (coefficient of determination, index of agreement greater than 0.8, and lower values of root mean square deviation) in most cases. The calibrated models were then employed to explore wheat yield and water productivity (WP) in response to irrigation and nitrogen fertilization applications. Scenario analyses indicated that water productivity and yield of wheat ranged from 1.2–2.0 kg m^–3 and 6.8–8.7 t ha^–1, respectively. Application of 0.8 from actual evapotranspiration and 120% from recommended nitrogen dose was the best-predicted scenario achieving the highest value of crop WP. Investigating the suitable option achieving the current wheat yield by farmers (7.4 t ha^–1), models demonstrated that application of 1.4 from actual evapotranspiration with 80% of the recommended nitrogen dose was the best option to achieve this yield. At this point, predicted WP was low and recorded 1.5 kg m^–3. Quantifying wheat yield in all districts of the studied area was also predicted using both models. APSIM-Wheat and AQUACROP can be used to drive the best management strategies in terms of N fertilizer and water regime for wheat under Egyptian conditions.

The present study aimed to investigate the potentiality of certain biocontrol agents, namely Bacillus subtilis, B. pumilus, B. megaterium, Pseudomonas fluorescens, Serratia marcescens, Trichoderma album, T. harzianum and T. viride, as well as the synthetic fungicide difenoconazole to control celery powdery mildew caused by Erysiphe heraclei DC, in vitro (against conidia germination and germ tube length of E. heraclei) and in vivo (against disease severity and AUDPC). In vitro, it was found that the antifungal activity of the tested biocontrol agents significantly reduced the germination percentage of the conidia and germ tube length of the pathogen. The reduction in conidia germination ranged between 88.2% and 59.6% as a result of the treatment with B. subtilis and T. album, respectively compared with 97.1% by the synthetic fungicide difenoconazole. Moreover, the fungicide achieved the highest reduction in germ tube length (92.5%) followed by B. megaterium (82.0%), while T. album was the least effective (62.8%). Spraying celery plants with the tested biocontrol agents in the greenhouse significantly reduced powdery mildew severity, as well as the area under the disease progress curve (AUDPC), after 7, 14, 21 and 28 days of application. In this regard, B. subtilis was the most efficient followed by B. pumilus, S. marcescens and B. megaterium, with 80.1, 74.4, 73.2 and 70.5% reductions in disease severity, respectively. In AUDPC, reductions of those microorganisms were 285.3, 380.9, 396.7 and 431.8, respectively, compared to 1539.1 in the control treatment. On the other hand, the fungicide difenoconazole achieved maximum efficacy in reducing disease severity (84.7%) and lowest AUDPC (219.3) compared to the other treatments. In the field, all the applied biocontrol agents showed high efficiency in suppressing powdery mildew on celery plants, with a significant improvement in growth and yield characteristics. In addition, they caused an increase in the concentration of leaf pigments, and the activities of defense-related enzymes such as peroxidase (PO) and polyphenol oxidase (PPO) and total phenol content (TPC). In conclusion, the results showed the possibility of using tested biocontrol agents as eco-friendly alternatives to protect celery plants against powdery mildew.

To study the effect of potassium nitrate, calcium nitrate and kaolin (Aluminum silicate) on pomegranate cv. Wonderful, this study was conducted during 2020–2021 to investigate the possibility of minimizing the percentages of sunburn and fruit cracking and ameliorating the yield and fruit quality of pomegranate during the aforementioned period. Four sprays consisting of potassium nitrate at 1%, 2% and 3%, calcium nitrate at 2%, 3% and 4%, kaolin at 2%, 4% and 6% and water only (control) were sprayed on pomegranate trees during May, July, and August. The results showed that through spraying the fruit at set percentages, fruit yield was greatly increased through the spraying of potassium nitrate, calcium nitrate and kaolin, particularly the application of potassium nitrate at 3% and 4% and kaolin at 6% as opposed to than the other percentages. In addition, the percentages of fruit cracking and sunburn were markedly lessened by the application of calcium nitrate at 4% and 6% and also by kaolin at 6%. Moreover, the fruit content from TSS, total sugars and anthocyanin, was improved through the spraying of potassium nitrate at 2% and 3%, whereas the fruit weight and firmness were improved by the application of calcium nitrate at 4% and kaolin at 6%.

The excessive use of chemical fertilizers in fruit orchards has led to numerous problems for the environment, produce quality, and food safety. It also negatively affects soil health, beneficial microorganisms, and ground water quality, hence the resurgence of the application of biostimulants as ecofriendly ways to improve the growth, yield, and fruit quality of tree fruits. The current study was performed during 2021 and 2022 to investigate the influence of foliar spraying of 500, 1000, and 2000 mg/L humic acid (HA); 0.5, 1, and 2 mg/L brassinosteroids (Brs); and 1000, 2000, and 3000 mg/L seaweed extract (SWE) compared with a control (untreated trees) in terms of the performance of an apricot (Prunus armeniaca) cv. Canino. The obtained results show that the spraying of HA, Brs, and SWE positively increased the shoot length, leaf area, leaf chlorophyll content, fruit set, fruit yields, and fruit physical and chemical characteristics, as well as leaf macro- or micronutrients contents compared with those untreated trees during both study years. Moreover, the increase in parameter values was parallel to the increase in the used concentrations of HA, Brs, or SWE, where 2000 mg/L HA, 2 mg/L Brs, and 3000 mg/L SWE were superior to 1000 mg/L HA, 1 mg/L Brs, and 2000 mg/L SWE, which were better than 500 mg/L HA, 0.5 mg/L Brs, and 1000 mg/L SWE.

Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for TSS, acidity, VitC, Tsugar, and Rsugar from fresh citrus fruits by applying different independent variables, namely the dimensions of the fruits (length (FL) and diameter (FD)), fruit weight (FW), yield/tree, and soil electrical conductivity (EC). The results of ANN application showed that a feed-forward back-propagation network type with four input neurons (Yield/tree, FW, FL, and FD) and eight neurons in one hidden layer provided successful modeling efficiencies for TSS, acidity, VitC, Tsugar, and Rsugar. The effect of the EC variable was not significant. The hyperbolic tangent of both the hidden layer and the output layer of the developed ANN model was chosen as the activation function. Based on statistical criteria, the ANN developed in this study performed better than the MLR model in predicting the chemical attributes of fresh citrus fruits. The root mean square error of TSS, acidity, VitC, Tsugar, and Rsugar ranged from 0.064 to 0.453 and 0.068 to 0.634, respectively, for the ANN model, and 0.568 to 4.768 and 0.550 to 4.830, respectively, for the MLR model using training and testing datasets. In addition, the relative errors obtained through the ANN approach provided high model predictability and feasibility. In chemical attribute modeling, the FD and FL variables exhibited high contribution ratios, resulting in a reliable predictive model. The developed ANN model generally showed a good level of accuracy when estimating the chemical attributes of fresh citrus fruit.