Giulia Zardinoni

(1) Background: Soil nutrient availability in vineyards is shaped by physicochemical and biological processes. However, how baseline edaphic differences are related to soil microbial functional genes and plant elemental composition under biodynamic management remains unclear; (2) Methods: Two biodynamically managed Pinot Noir (Vitis vinifera L.) vineyard sites in Tasmania, hereafter referred to as site 1 (S1) and site 2 (S2), were compared at fruit set, veraison, and ripening. Soil physicochemical properties were measured, soil, leaf, and grape berry elemental compositions were assessed by X-ray fluorescence, and soil microbial taxonomic marker genes and soil microbial functional genes were quantified by qPCR. Because the dataset comprised only six site-by-stage composite samples without independent field-level biological replication, multivariate analyses and partial least squares path modeling were used as exploratory tools; (3) Results: The two sites showed distinct baseline soil physicochemical properties. Soil microbial functional genes varied across sites and phenological stages, with several nitrogen (N)-cycling genes showing higher values at S1 and amoA increasing toward ripening at both sites. AMG, defined here as an arbuscular mycorrhizal fungal (AMF)-related marker, also increased toward ripening and was interpreted separately from the N-cycling genes. Soil elements mainly reflected site-related differences, whereas leaf and berry elements showed clearer variation across phenological stages. The exploratory path model, based on this limited composite dataset, summarized sequential associations among soil physicochemical properties, microbial functional genes, leaf elements, and berry elements, as well as a direct association between soil physicochemical properties and berry elemental composition; (4) Conclusions: These findings describe exploratory soil–microbe–plant association patterns under biodynamic management and should not be interpreted as statistically inferential or causal evidence.

(1) Background: Soil fertility in organic systems depends on interactions between physicochemical properties and biological processes that regulate nutrient availability along the soil profile. However, information on their vertical distribution remains limited, particularly for root crops such as sugar beet. This study evaluated depth-related patterns in soils from three organic farms growing sugar beet. (2) Methods: Soil profiles (0–120 cm) were sampled and analyzed for physicochemical properties, mineral nitrogen (N) forms, and biological indicators, including the QBS-ar index, microbial abundance, and functional genes involved in N and carbon cycling. (3) Results: Nitrate-N and total mineral N were mainly concentrated in the 0–40 cm layer and declined markedly with depth. Microbial abundance and most N-cycling functional genes were similarly enriched in the topsoil, showing clear vertical stratification. Statistical analyses suggested that functional gene composition was associated with mineral N gradients after accounting for soil depth. (4) Conclusions: These findings provide an exploratory indication of relationships between mineral N forms and microbial indicators in an organically managed sugar beet system. Given the limited number of sampling units, results should be interpreted cautiously. However, these results highlight the value of soil profile approaches for understanding N redistribution and improving nutrient management strategies.

Interactions among plant roots, soil, and microorganisms in the rhizosphere regulate nutrient cycling, plant health, and ecosystem resilience. Recent advances in DNA sequencing and multi-omics are contributing to a shift from primarily descriptive surveys toward more mechanistic and predictive frameworks. This review synthesizes methodological developments and conceptual insights spanning microbial ecology, functional genomics, and agricultural applications. We first summarize DNA-based approaches—marker-gene sequencing, shotgun metagenomics, and quantitative nucleic acid assays—and then complementary omics layers, including metatranscriptomics, metaproteomics, metabolomics, epigenomics, ionomics, and phenomics. We next outline computational advances in data integration, network modeling, and visualization that help represent complex multi-layered datasets as biologically interpretable systems. Applications relevant to climate resilience and sustainable agriculture are discussed, including the design of synthetic microbial communities, the identification of biomarkers for soil health and stress tolerance, and case studies in which rhizosphere multi-omics informs crop breeding and soil management strategies. Overall, these developments underscore the potential of treating microbes as functional and, to some extent, manageable components of the plant holobiont. Looking ahead, we identify key research gaps involving standardized workflows, cross-scale causal inference, and real-time monitoring pipelines that integrate molecular diagnostics with remote sensing and edge–cloud analytics. By linking ecological mechanisms with translational practice, multi-omics frameworks may support the development of more sustainable, data-driven agriculture that better aligns productivity with environmental stewardship.