With climate change escalating to global proportions over the past decade, along with a growing population, methods are being sought to help the natural and cultivated environment function in an ecologically balanced manner. Over the past few years, there has been a significantly increased interest in research on the use of natural substances for sustainable agriculture and horticulture. One of the most effective solutions to the emerging need is biochar, which has been the subject of environmental research for years due to its potential to increase soil carbon sequestration, reduce greenhouse gas emissions, remediate contaminated soil, and alleviate anthropogenic pressures. There is evidence of improved soil fertility and increased crop yields in agricultural production after biochar application. Our work comprehensively describes the effects of biochar on soil properties, crop productivity, and mitigating environmental stresses, and its remediation potential in heavy metal-contaminated soils. We analyzed a wide range of the literature on the most important properties of biochar for various potential uses. We summarized the results of research work over the past two decades to analyze soil and plant responses to biochar application.

Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security.

One of the main causes of climate change is the emission of GHGs, and one of the sources for the generation of such gasses is agriculture via plant production. Considering the foregoing, a study was conducted to assess PGPRs in strawberry cultivation which were able to limit GHG emissions. The first experimental factor was the inoculation of plant roots with the Bacillus sp. strains DLGB3, DKB26, DKB58, and DKB 84; the Pantoea sp. strains DKB63, DKB64, DKB65, and DKB68; Azotobacter sp. AJ 1.2; and Pseudomonas sp. PJ 1.1. The second experimental factor constituted the different moisture levels of the growth substrate. In the experiment, emissions of NH₃, CO₂, N₂O, and CH₄ were measured. In light of the conducted research, five strains were selected (Azotobacter sp. AJ 1.2; Pantoea sp. DKB64, DKB63, and DKB68; and Pseudomonas sp. strain PJ 1.1) that showed the greatest potential for reducing GHG emissions depending on the prevailing environmental conditions. The application of the tested bacterial strains under different moisture conditions in the substrate either reduced or did not affect GWP. This research on PGPR, which was conducted to select strains of rhizosphere bacteria that would be able to reduce GHG emissions, may form the basis for creating an inoculum and can be employed as an effective strategy for mitigating certain abiotic stresses.

One of the most invasive freshwater fish in Europe is the stone moroko (Pseudorasbora parva), but its potential for food use remains largely unexplored. Fish were collected from ponds (Raszyn, Stare Kurowo) and a natural river (Wardynka). The study measured fish weight and length, calculated the Fulton coefficient, determined the chemical composition of the meat, energy value plus yield. Heavy metal content was determined after HNO₃ digestion using GFAAS method. Health risk was assessed using EDI, THQ, HI, CR. The data were statistically analyzed. No differences in length were found. Individuals from Stare Kurowo and Wardynka had significantly higher body weights than those from Raszyn. Wardynka fish had the highest water content (74.83%), the lowest dry matter, protein, and fat levels. All samples contained low concentrations of heavy metals, with the lowest levels in Wardynka and the highest in Raszyn. Estimated daily intake (EDI) values ranged from 0.28% to 3.03% of the reference dose, remaining under 1% in most cases. Nutrient levels matched typical European freshwater fish. Differences among sites likely stemmed from food availability and competition. The low metal content and favourable meat quality indicate potential for food production, helping control this invasive species and protect native fish.