This review delves into the role of plant growth-promoting microbes (PGPMs) in sustainable agriculture by enhancing soil health, improving plant stress tolerance, and controlling diseases. PGPMs, which include bacteria, fungi, and other microorganisms, are increasingly recognized as important contributors to promoting eco-friendly agricultural practices. Their ability to improve nutrient availability, stimulate plant growth, and protect crops from environmental stressors makes them a cornerstone for resilient and efficient farming systems. By reducing dependency on synthetic fertilizers and pesticides, PGPMs play a vital role in preserving environmental resources, minimizing greenhouse gas (GHG) emissions, and combating climate change’s negative impacts. This article thoroughly explores the complex interactions between PGPMs, soil nutrients, plant pathogens, and abiotic stressors. It also examines how these interactions influence phytohormone production and signaling pathways, ultimately impacting crop growth and development. Furthermore, this review discusses how PGPMs enhance nutrient uptake, modulate plant immune responses, and improve stress resilience, offering a holistic understanding of their multifaceted contributions to farming. By synthesizing current research and highlighting future directions, this review underscores the potential of PGPMs to revolutionize agricultural activities, ensuring food security and environmental sustainability in the face of global challenges.

Precision agriculture (PA) is a data-driven, technology-enabled farming management strategy that monitors, quantifies, and examines the requirements of specific crops and fields. A key aim of precision agricultural technologies is to optimize crop yield and quality, while also working to lower operating costs and minimize environmental impact. This approach not only enhances productivity but also promotes sustainable farming practices. In PA, it is essential to leverage effective monitoring through sensing technologies, implement robust management information systems, and proactively address both inter- and intravariability within cropping systems. Crop yield simulations using deep learning and machine learning (ML) techniques aid in understanding the combined effects of pests, nutrient and water shortages, and other field variables during the growing season. On the other hand, remote sensing techniques such as lidar imagery, radar, and multi- and hyperspectral data presents valuable opportunities to enhance yield predictions by improving the understanding of soil, climate, and other biophysical factors affecting crops. This paper aims to highlight key gaps and opportunities for future research, focusing on the evolving landscape of remote sensing and machine learning techniques employed to enhance predictions of crop yield. In future, PA is likely to include more focused use of sensor platforms and ML techniques can enhance the effectiveness of agricultural practices. Additionally, the development of hybrid systems that combine diverse ML approaches and signal processing techniques will pave the way for more innovative and efficient solutions in the field.

Wheat is considered to be a strategic crop for achieving food security. Wherefore, one of the current objectives of today’s agriculture is to ensure a consistent and sustainable yield of this particular crop while mitigating its environmental footprint. However, along with the genetic potential of varieties, agricultural practices play a key role in ensuring a high and stable yield of wheat. Under changing climatic conditions, new eco-friendly practices were adopted in the wheat farming system in recent decades. In this review, a large number of peer-reviewed articles have been screened during the last 15 years to evaluate the potential of some environmentally friendly agricultural practices such as tillage system, biological crop protection, crop rotation, intercropping systems, and the integration of resistant varieties in achieving a high and stable wheat yield. The present investigation unveiled that embracing eco-friendly agricultural methods in the wheat farming system holds the potential to engender high and sustainable wheat yields, contingent upon a normative strategy that comprehensively addresses multiple factors. These include the intrinsic attributes of the grown wheat cultivars, plant nutritional parameters, soil agrochemical characteristics, and specific climatic conditions. Further in-depth investigations under field conditions are necessary to help in the discernment of appropriate environmentally agricultural techniques that can efficaciously optimize the yield potential of the different cultivated varieties.

Little is known about how the performance of legumes symbiosis affects biomass and nutrient accumulation by intercropped cereals under the field condition. To assess the agricultural services of an intercropping system; durum wheat (Triticum turgidum durum L.cv. VITRON) and chickpea (Cicer arietinum L.cv. FLIP 90/13 C) were cultivated as both intercrops and sole cropping during two growing seasons under the field trial, to compare plant biomass, nodulation, N and phosphorus (P) uptake, and N nutrition index. Both the above-ground biomass and grain yield and consequently, the amount of N taken up by intercropped durum wheat increased significantly (44%, 48%, and 30%, respectively) compared with sole cropping during the two seasons. However, intercropping decreased P uptake by both durum wheat and chickpea. The efficiency in use of rhizobial symbiosis (EURS) for intercropped chickpea was significantly higher than for chickpea grown as sole cropping. The intercropped chickpea considerably increased N (49%) and P (75%) availability in durum wheat rhizosphere. In the case of chickpea shoot, the N nutrition (defined by the ratio between actual and critical N uptake by crop) and acquisition were higher in intercropping during only the first year of cropping. Moreover, biomass, grin yield, and resource (N and P) use efficiency were significantly improved, as indicated by higher land equivalent ratio (LER > 1) in intercropping over sole cropping treatments. Our findings suggest that change in the intercropped chickpea rhizosphere-induced parameters facilitated P and N uptake, above-ground biomass, grain yield, and land use efficiency for wheat crop.

Recent decades have witnessed a danger to food security as well as to human health because of pollutants’ negative impact on crop quality. An accurate estimate of the heavy metal concentrations in Egypt’s north Nile Delta is required to lower the high concentration levels of heavy metal in the soil as a means to develop a remediation strategy that stabilizes heavy metals in contaminated soil. Using a geo-accumulation index (I-geo), contamination factor (CF), Improved Nemerow’s Pollution Index (Pn), and Potential Ecological Risk Index (PERI), supported by GIS; principal component analysis (PCA), and cluster analysis, six heavy metals (As, Co, Cu, Ni, V, and Zn) were analyzed from 15 soil profile layers (61 soil samples) to determine the extent of the soil contamination in the area studied. The findings demonstrate the widespread I-geo contamination of As, Co, Cu, Ni, V, and Zn in different layers. The ranges for the I-geo values were from −8.2 to 5.3; 4.11 to 1.8; 6.4 to 1.9; −9.7 to 2.8; −6.3 to 2.9; and from −12.5 to 2.4 for As, Co, Cu, Ni, V, and Zn, respectively. I-geo categorization therefore ranged from uncontaminated to strongly/extremely contaminated. The CF values varied from 0.01 to 60.6; 0.09 to 5.17; 0.02 to 10.51; 0 to 10.51; 0.02 to 7.12; and 0 to 7.68 for As, Co, Cu, Ni, V, and Zn, respectively. In decreasing sequence, the CFs are arranged as follows: CF (As), CF (Ni), CF (Zn), CF (V), CF (Cu), and CF (Co). Most of the research region (71.9%) consisted of a class of moderately to heavily polluted areas. Additionally, a large portion of the study region (49.17%) has a very high risk of contamination, as per the results of the PERI index. The use of a correlation matrix, cluster analysis, and principal component analysis(PCA) to evaluate the variability in the soil’s chemical content revealed the impact from anthropogenic activities on the heavy metal concentration levels in the study area’s soil. The current findings reflect the poor quality of management in the research region, which led to the increase in the concentration of heavy metals in the soil. Decision-makers could use the outcomes from the spatial distribution maps for contaminants and their levels as a basis for creating heavy metal mitigation strategies.

One of the most significant challenges that global decision-makers are concerned about is soil contamination. It is also related to food security and soil fertility. The quality of the soil and crops in Egypt are being severely impacted by the increased heavy metal content of the soils in the middle Nile Delta. In Egypt’s middle Nile Delta, fifty random soil samples were chosen. Inverse distance weighting (IDW) was used to create the spatial pattern maps for four heavy metals: Cd, Mn, Pb, and Zn. The soil contamination levels in the research area were assessed using principal component analysis (PCA), contamination factors (CF), the geoaccumulation index (I-Geo), and the improved Nemerow pollution index (I_n). The findings demonstrated that using PCA, the soil heavy metal concentrations were divided into two clusters. Moreover, the majority of the study region (44.47%) was assessed to be heavily to extremely polluted by heavy metals. In conclusion, integrating the contamination indices CF, I-Geo, and In with the GIS technique and multivariate model, analysis establishes a practical and helpful strategy for assessing the hazard of heavy metal contamination. The findings could serve as a basis for decision-makers to create effective heavy metal mitigation efforts.