Overexploitation of resources makes the reutilization of waste a focal topic of modern society, and the question of the kind of wastes that can be used is continuously raised. Sewage sludge (SS) is derived from the wastewater treatment plants, considered important underused biomass, and can be used as a biofertilizer when properly stabilized due to the high content of inorganic matter, nitrate, and phosphorus. However, a wide range of pollutants can be present in these biosolids, limiting or prohibiting their use as biofertilizer, depending on the type and origin of industrial waste and household products. Long-term applications of these biosolids could substantially increase the concentration of contaminants, causing detrimental effects on the environment and induce hyperaccumulation or phytotoxicity in the produced crops. In this work, some critical parameters for soils and SS agronomic use, such as organic matter, nitrogen, phosphorous, and potassium (NPK), and heavy metals concentration have been reviewed. Several cases of food crop production and the accumulation of heavy metals after SS application are also discussed. SS production, usage, and legislation in EU are assessed to determine the possibility of sustainable management of this bioresource. Additionally, the World Health Organization (WHO) and Food and Agriculture Organization (FAO) guidelines are addressed. The opportunity to produce bioenergy crops, employing sewage sludge to enhance degraded land, is also considered, due to energy security. Although there are numerous advantages of sewage sludge, proper screening for heavy metals in all the variants (biosolids, soil, food products) is a must. SS application requires appropriate strict guidelines with appropriate regulatory oversight to control contamination of agricultural soils.

Reverse transcription-quantitative real-time PCR (RT-qPCR) is a widely used technique for gene expression analysis. The reliability of this method depends largely on the suitable selection of stable reference genes for accurate data normalization. Hypericum perforatum L. (St. John's wort) is a field growing plant that is frequently exposed to a variety of adverse environmental stresses that can negatively affect its productivity. This widely known medicinal plant with broad pharmacological properties (anti-depressant, anti-tumor, anti-inflammatory, antiviral, antioxidant, anti-cancer, and antibacterial) has been overlooked with respect to the identification of reference genes suitable for RT-qPCR data normalization. In this study, 11 candidate reference genes were analyzed in H. perforatum plants subjected to cold and heat stresses. The expression stability of these genes was assessed using GeNorm, NormFinder and BestKeeper algorithms. The results revealed that the ranking of stability among the three algorithms showed only minor differences within each treatment. The best-ranked reference genes differed between cold- and heat-treated samples; nevertheless, TUB was the most stable gene in both experimental conditions. GSA and GAPDH were found to be reliable reference genes in cold-treated samples, while GAPDH showed low expression stability in heat-treated samples. 26SrRNA and H2A had the highest stabilities in the heat assay, whereas H2A was less stable in the cold assay. Finally, AOX1, AOX2, CAT1 and CHS genes, associated with plant stress responses and oxidative stress, were used as target genes to validate the reliability of identified reference genes. These target genes showed differential expression profiles over time in treated samples. This study not only is the first systematic analysis for the selection of suitable reference genes for RT-qPCR studies in H. perforatum subjected to temperature stress conditions, but may also provide valuable information about the roles of genes associated with temperature stress responses.

Molecular plant breeding usually overlooks the genetic variability that arises from the association of plants with endophytic microorganisms, when looking at agronomic interesting target traits. This source of variability can have crucial effects on the functionality of the organism considered as a whole (the holobiont), and therefore can be selectable in breeding programs. However, seeing the holobiont as a unit for selection and improvement in breeding programs requires novel approaches for genotyping and phenotyping. These should not focus just at the plant level, but also include the associated endophytes and their functional effects on the plant, to make effective desirable trait screenings. The present review intends to draw attention to a new research field on functional hologenomics that if associated with adequate phenotyping tools could greatly increase the efficiency of breeding programs.

The advancement of technology associated with the field, especially the use of unmanned aerial vehicles (UAV) coupled with multispectral cameras, allows us to monitor the condition of crops in real time and contribute to the field of machine learning. The objective of this study was to estimate both productivity and above-ground biomass (AGB) for the corn crop by applying different vegetation indices (VIs) via high-resolution aerial imagery. Among the indices tested, strong correlations were obtained between productivity and the normalized difference vegetation index (NDVI) with a significance level of p < 0.05 (0.719), as well as for the normalized difference red edge (NDRE), or green normalized difference vegetation index (GNDVI) with crop productivity (p < 0.01), respectively 0.809 and 0.859. The AGB results align with those obtained previously; GNDVI and NDRE showed high correlations, but now with a significance level of p < 0.05 (0.758 and 0.695). Both GNDVI and NDRE indices showed coefficients of determination for productivity and AGB estimation with 0.738 and 0.654, and 0.701 and 0.632, respectively. The use of the GNDVI and NDRE indices shows excellent results for estimating productivity as well as AGB for the corn crop, both at the spatial and numerical levels. The possibility of predicting crop productivity is an essential tool for producers, since it allows them to make timely decisions to correct any deficit present in their agricultural plots, and further contributes to AI integration for drone digital optimization.