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 productivity of cereal crops under salt stress limits sustainable food production and food security. Barley followed by sorghum better adapts to salinity stress, while wheat and maize are moderately adapted. However, rice is a salt-sensitive crop, and its growth and grain yield are significantly impacted by salinity stress. High soil salinity can reduce water uptake, create osmotic stress in plants and, consequently, oxidative stress. Crops have evolved different tolerance mechanisms, particularly cereals, to mitigate the stressful conditions, i.e., effluxing excessive sodium (Na⁺) or compartmentalizing Na⁺ to vacuoles. Likewise, plants activate an antioxidant defense system to detoxify apoplastic cell wall acidification and reactive oxygen species (ROS). Understanding the response of field crops to salinity stress, including their resistance mechanisms, can help breed adapted varieties with high productivity under unfavourable environmental factors. In contrast, the primary stages of seed germination are more critical to osmotic stress than the vegetative stages. However, salinity stress at the reproductive stage can also decrease crop productivity. Biotechnology approaches are being used to accelerate the development of salt-adapted crops. In addition, hormones and osmolytes application can mitigate the toxicity impact of salts in cereal crops. Therefore, we review the salinity on cereal crops physiology and production, the management strategies to cope with the harmful negative effect on cereal crops physiology and production of salt stress.

The concentration of greenhouse gases (GHGs) in the atmosphere has been increasing since the beginning of the industrial revolution. Nitrous oxide (N₂O) is one of the mightiest GHGs, and agriculture is one of the main sources of N₂O emissions. In this paper, we reviewed the mechanisms triggering N₂O emissions and the role of agricultural practices in their mitigation. The amount of N₂O produced from the soil through the combined processes of nitrification and denitrification is profoundly influenced by temperature, moisture, carbon, nitrogen and oxygen contents. These factors can be manipulated to a significant extent through field management practices, influencing N₂O emission. The relationships between N₂O occurrence and factors regulating it are an important premise for devising mitigation strategies. Here, we evaluated various options in the literature and found that N₂O emissions can be effectively reduced by intervening on time and through the method of N supply (30–40%, with peaks up to 80%), tillage and irrigation practices (both in non-univocal way), use of amendments, such as biochar and lime (up to 80%), use of slow-release fertilizers and/or nitrification inhibitors (up to 50%), plant treatment with arbuscular mycorrhizal fungi (up to 75%), appropriate crop rotations and schemes (up to 50%), and integrated nutrient management (in a non-univocal way). In conclusion, acting on N supply (fertilizer type, dose, time, method, etc.) is the most straightforward way to achieve significant N₂O reductions without compromising crop yields. However, tuning the rest of crop management (tillage, irrigation, rotation, etc.) to principles of good agricultural practices is also advisable, as it can fetch significant N₂O abatement vs. the risk of unexpected rise, which can be incurred by unwary management.

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.

Agriculture plays a crucial role in the economy, and there is an increasing global emphasis on automating agricultural processes. With the tremendous increase in population, the demand for food and employment has also increased significantly. Agricultural methods traditionally used to meet these requirements are no longer adequate, requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers. Integration of technologies such as the Internet of Things, wireless communication, machine learning, artificial intelligence (AI), and deep learning shows promise in addressing these challenges. However, there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 327 initially identified articles, 180 were deemed pertinent, focusing primarily on AI’s potential in enhancing yield through better management of nutrients, water, and weeds. Taking into account research findings worldwide, we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and determine the best time for irrigation or herbicide application. The present status of AI-driven automation in agriculture holds significant promise for optimizing agricultural input utilization and reducing resource waste, particularly in the context of three pillars of crop management, i.e., nutrient, irrigation, and weed management.

Crop simulation models can be effective tools to assist with optimization of resources for a particular agroecological zone. The goal of this study was to determine the influence of N rates with different timing of application to wheat crop using prominent varieties using the CSM-CERES-Wheat model of the decision support system for agrotechnology transfer (DSSAT). Data were focused for yield traits, i.e., number of tillers, number of grains, grain weight, grain yield, biomass, and grain N content. To test the applicability of the CSM-CERES-Wheat version 4.7.5 model for agroclimatic conditions of Peshawar, Pakistan, experimental data from two years of experiments (2016–17 and 2017–18) were used for model calibration and evaluation. The simulation results of two years agreed well with field measured data for three commercial varieties. The model efficiency (R²) for wheat varieties was above 0.94 for variables tiller number per unit area (m⁻²), number of grains (m⁻²) and number of grains (spike⁻¹), 1000 grain weight (mg), biomass weight (kg ha⁻¹), grain yield (kg ha⁻¹), and harvest N content (kg ha⁻¹). Statistics of cultivars indicated that yield traits, yield, and N can be simulated efficiently for agroecological conditions of Peshawar. Moreover, different N rates and application timings suggested that the application of 140 kg N ha⁻¹ with triple splits timings, i.e., 25% at the sowing, 50% at the tillering, and 25% at the booting stage of the crop, resulted in the maximum yield and N recovery for different commercial wheat varieties. Simulated N losses, according to the model, were highly determined by leaching for experimental conditions where a single N application of 100% or existing double splits timing was applied. The study concluded that 140 kg N ha⁻¹ is most appropriate for wheat crop grown on clay loam soils under a flood irrigation system. However, the N fertilizer has to be given in triple splits of a 1:2:1 ratio at the sowing, tillering, and booting stages of the crop growth.