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Population growth and climate change challenge our food and farming systems and provide arguments for an increased intensification of agriculture. A promising option is eco-functional intensification through organic farming, an approach based on using and enhancing internal natural resources and processes to secure and improve agricultural productivity, while minimizing negative environmental impacts. In this concept an active soil microbiota plays an important role for various soil based ecosystem services such as nutrient cycling, erosion control and pest and disease regulation. Several studies have reported a positive effect of organic farming on soil health and quality including microbial community traits. However, so far no systematic quantification of whether organic farming systems comprise larger and more active soil microbial communities compared to conventional farming systems was performed on a global scale. Therefore, we conducted a meta-analysis on current literature to quantify possible differences in key indicators for soil microbial abundance and activity in organic and conventional cropping systems. All together we integrated data from 56 mainly peer-reviewed papers into our analysis, including 149 pairwise comparisons originating from different climatic zones and experimental duration ranging from 3 to more than 100 years. Overall, we found that organic systems had 32% to 84% greater microbial biomass carbon, microbial biomass nitrogen, total phospholipid fatty-acids, and dehydrogenase, urease and protease activities than conventional systems. Exclusively the metabolic quotient as an indicator for stresses on microbial communities remained unaffected by the farming systems. Categorical subgroup analysis revealed that crop rotation, the inclusion of legumes in the crop rotation and organic inputs are important farming practices affecting soil microbial community size and activity. Furthermore, we show that differences in microbial size and activity between organic and conventional farming systems vary as a function of land use (arable, orchards, and grassland), plant life cycle (annual and perennial) and climatic zone. In summary, this study shows that overall organic farming enhances total microbial abundance and activity in agricultural soils on a global scale.

Root-associated microbes play a key role in plant performance and productivity, making them important players in agroecosystems. So far, very few studies have assessed the impact of different farming systems on the root microbiota and it is still unclear whether agricultural intensification influences the structure and complexity of microbial communities. We investigated the impact of conventional, no-till, and organic farming on wheat root fungal communities using PacBio SMRT sequencing on samples collected from 60 farmlands in Switzerland. Organic farming harbored a much more complex fungal network with significantly higher connectivity than conventional and no-till farming systems. The abundance of keystone taxa was the highest under organic farming where agricultural intensification was the lowest. We also found a strong negative association ( R 2  = 0.366; P  < 0.0001) between agricultural intensification and root fungal network connectivity. The occurrence of keystone taxa was best explained by soil phosphorus levels, bulk density, pH, and mycorrhizal colonization. The majority of keystone taxa are known to form arbuscular mycorrhizal associations with plants and belong to the orders Glomerales , Paraglomerales , and Diversisporales . Supporting this, the abundance of mycorrhizal fungi in roots and soils was also significantly higher under organic farming. To our knowledge, this is the first study to report mycorrhizal keystone taxa for agroecosystems, and we demonstrate that agricultural intensification reduces network complexity and the abundance of keystone taxa in the root microbiome.

It has been suggested that conversion to organic farming contributes to soil carbon sequestration, but until now a comprehensive quantitative assessment has been lacking. Therefore, datasets from 74 studies from pairwise comparisons of organic vs. nonorganic farming systems were subjected to metaanalysis to identify differences in soil organic carbon (SOC). We found significant differences and higher values for organically farmed soils of 0.18 ± 0.06% points (mean ± 95% confidence interval) for SOC concentrations, 3.50 ± 1.08 Mg C ha ⁻¹ for stocks, and 0.45 ± 0.21 Mg C ha ⁻¹ y ⁻¹ for sequestration rates compared with nonorganic management. Metaregression did not deliver clear results on drivers, but differences in external C inputs and crop rotations seemed important. Restricting the analysis to zero net input organic systems and retaining only the datasets with highest data quality (measured soil bulk densities and external C and N inputs), the mean difference in SOC stocks between the farming systems was still significant (1.98 ± 1.50 Mg C ha ⁻¹), whereas the difference in sequestration rates became insignificant (0.07 ± 0.08 Mg C ha ⁻¹ y ⁻¹). Analyzing zero net input systems for all data without this quality requirement revealed significant, positive differences in SOC concentrations and stocks (0.13 ± 0.09% points and 2.16 ± 1.65 Mg C ha ⁻¹, respectively) and insignificant differences for sequestration rates (0.27 ± 0.37 Mg C ha ⁻¹ y ⁻¹). The data mainly cover top soil and temperate zones, whereas only few data from tropical regions and subsoil horizons exist. Summarizing, this study shows that organic farming has the potential to accumulate soil carbon.

Agricultural practices contribute considerably to emissions of greenhouse gases. So far, knowledge on the impact of organic compared to non-organic farming on soil-derived nitrous oxide (N 2 O) and methane (CH 4 ) emissions is limited. We investigated N 2 O and CH 4 fluxes with manual chambers during 571 days in a grass-clover– silage maize – green manure cropping sequence in the long-term field trial “DOK” in Switzerland. We compared two organic farming systems – biodynamic (BIODYN) and bioorganic (BIOORG) – with two non-organic systems – solely mineral fertilisation (CONMIN) and mixed farming including farmyard manure (CONFYM) – all reflecting Swiss farming practices–together with an unfertilised control (NOFERT). We observed a 40.2% reduction of N 2 O emissions per hectare for organic compared to non-organic systems. In contrast to current knowledge, yield-scaled cumulated N 2 O emissions under silage maize were similar between organic and non-organic systems. Cumulated on area scale we recorded under silage maize a modest CH 4 uptake for BIODYN and CONMIN and high CH 4 emissions for CONFYM. We found that, in addition to N input, quality properties such as pH, soil organic carbon and microbial biomass significantly affected N 2 O emissions. This study showed that organic farming systems can be a viable measure contributing to greenhouse gas mitigation in the agricultural sector.

Increasing soil organic carbon (SOC) aims to increase and maintain soil quality for sustainable crop production and to achieve carbon removal targets. Agronomic practices are therefore needed to reduce carbon losses and increase SOC stocks, especially in deep soil layers, which promote long-term storage. Regenerative agriculture is an approach aimed at increasing soil quality for sustainable production and therefore should be suitable for achieving the required goals under organic farming conditions. We analysed SOC content down to a depth of 1 m and calculated the SOC stock based on bulk density after ten years of regenerative farming practices, i.e., reduced tillage, dead organic mulch, and high-quality yard waste compost application, in two organic field trials set up one year apart in Central Germany and we calculated the carbon (C) and nitrogen (N) input to the soil from organic amendments and from main crops and cover crops by applying C allocation factors for crop residues, roots, and rhizodeposition. C derived from crops was the main carbon input source over ten years. Increasing C input promoted an increase in the cumulative SOC stock down to 1 m. We observed greater SOC stocks dominated by topsoil changes with reduced tillage and compost application and with the combination of all practices (+ 16%) than in the control with conventional ploughing and no external carbon input while none of the farming practices affected the subsoil SOC stock. Mulch application had no effect at all on SOC stocks. Crop biomass contributed most C input. Farming practices, especially the combination of reduced tillage and compost application, enhanced topsoil SOC stocks and N but not subsoil C storage. Other farming practices and crop rotation adjustments must be identified to increase crop production as well as subsoil SOC stocks and promoting long-term C storage, e.g., by fostering deep-rooting crops and cover crops.

The application of microbial inoculants (biofertilizers) is a promising technology for future sustainable farming systems in view of rapidly decreasing phosphorus stocks and the need to more efficiently use available nitrogen (N). Various microbial taxa are currently used as biofertilizers, based on their capacity to access nutrients from fertilizers and soil stocks, to fix atmospheric nitrogen, to improve water uptake or to act as biocontrol agents. Despite the existence of a considerable knowledge on effects of specific taxa of biofertilizers, a comprehensive quantitative assessment of the performance of biofertilizers with different traits such as phosphorus solubilization and N fixation applied to various crops at a global scale is missing. We conducted a meta-analysis to quantify benefits of biofertilizers in terms of yield increase, nitrogen and phosphorus use efficiency, based on 171 peer reviewed publications that met eligibility criteria. Major findings are: (i) the superiority of biofertilizer performance in dry climates over other climatic regions (yield response: dry climate +20.0 ± 1.7%, tropical climate +14.9 ± 1.2%, oceanic climate +10.0 ± 3.7%, continental climate +8.5 ± 2.4%); (ii) meta-regression analyses revealed that yield response due to biofertilizer application was generally small at low soil P levels; efficacy increased along higher soil P levels in the order arbuscular mycorrhizal fungi (AMF), P solubilizers, and N fixers; (iii) meta-regressions showed that the success of inoculation with AMF was greater at low organic matter content and at neutral pH. Our comprehensive analysis provides a basis and guidance for proper choice and application of biofertilizers.

Plant roots are major transmitters of atmospheric carbon into soil. The rhizosphere, the soil volume around living roots influenced by root activities, represents hotspots for organic carbon inputs, microbial activity, and carbon turnover. Rhizosphere processes remain poorly understood and the observation of key mechanisms for carbon transfer and protection in intact rhizosphere microenvironments are challenging. We deciphered the fate of photosynthesis-derived organic carbon (OC) in intact wheat rhizosphere, combining stable isotope labeling at field scale with high-resolution 3D-imaging. We used nano-scale secondary ion mass spectrometry and focus ion beam-scanning electron microscopy to generate insights into rhizosphere processes at nanometer scale. In immature wheat roots, the carbon circulated through the apoplastic pathway, via cell walls, from the stele to the cortex. The carbon was transferred to substantial microbial communuties, mainly represented by bacteria surrounding peripheral root cells. Iron oxides formed bridges between roots and bigger mineral particles, such as quartz, and surrounded bacteria in microaggregates close to the root surface. Some microaggregates were also intimately associated with the fungal hyphae surface. Based on these results, we propose a conceptual model depicting the fate of carbon at biogeochemical interfaces in the rhizosphere, at the forefront of growing roots. We observed complex interplays between vectors (roots, fungi, bacteria), transferring plant-derived OC into root-free soil and stabilizing agents (iron oxides, root and microorganism products), potentially protecting plant-derived OC within microaggregates in the rhizosphere.

A greenhouse experiment with sorghum sudangrass (Sorghum bicolor × Sorghum sudanense) and maize (Zea mays) was conducted to assess information on differences in their nitrogen and fertilizer utilization when used as energy crops. The aim was to contribute to the scarce data on sorghum sudangrass as an energy crop with regards to nitrogen derived from fertilizer (NdfF) in the plant’s biomass and fertilizer nitrogen utilization (FNU). Sorghum sudangrass and maize were each grown in eight bags of 45 L volume and harvested at maturity after 154 days. Each crop treatment was further divided in a control treatment (four bags each) that did not receive N fertilization and a fertilization treatment (four bags each) that received 1.76 g N, applying a 15N-labelled liquid ammonium nitrate fertilizer. Fertilization took place at the start of the experiment. After harvest, the whole plant was divided in the fractions “aboveground biomass” (ABM) and “stubble + rootstock” (S + R). Weight, N content and 15N content were recorded for each fraction. In addition, N content and 15N content were assessed in the soil before sowing and after harvest. The experiment showed that FNU of sorghum sudangrass (65%) was significantly higher than that of maize (49%). Both crops accumulated more soil N than fertilizer N. The share of fertilizer N on total N uptake was also higher with sorghum sudangrass (NdfF = 38%) compared to maize (NdfF = 34%). The observations made with our control plant (maize), showed that the results are plausible and comparable to other 15N studies on maize regarding yields, NdfF, and FNU, leading to the assumption that results on sorghum sudangrass are plausible as well. We therefore conclude that the results of our study can be used for the preliminary parametrization of sorghum sudangrass in soil organic matter (SOM) balance at field level.

The intensification of food production systems has resulted in landscape simplification, with trees and hedges disappearing from agricultural land, principally in industrialized countries. However, more recently, the potential of agroforestry systems and small woody landscape features (SWFs), e.g., hedgerows, woodlots, and scattered groups of trees, to sequester carbon was highlighted as one of the strategies to combat global climate change. Our study was aimed to assess the extent of SWFs embedded within agricultural landscapes in Germany, estimate their carbon stocks, and investigate the potential for increasing agroforestry cover to offset agricultural greenhouse gas (GHG) emissions. We analyzed open-source geospatial datasets and identified over 900,000 hectares of SWFs on agricultural land, equivalent to 4.6% of the total farmland. The carbon storage of SWFs was estimated at 111 ± 52 SD teragrams of carbon (Tg C), which was previously unaccounted for in GHG inventories and could play a role in mitigating the emissions. Furthermore, we found cropland to have the lowest SWF density and thus the highest potential to benefit from the implementation of agroforestry, which could sequester between 0.2 and 2 Tg of carbon per year. Our study highlights that country-specific data are urgently needed to refine C stock estimates, improve GHG inventories and inform the large-scale implementation of agroforestry in Germany.

Soil organic carbon (SOC) monitoring is central to carbon farming Monitoring, Reporting, and Verification (MRV), yet high laboratory costs and sparse sampling limit its scalability. We present the first independent field validation of the Stenon FarmLab multi-sensor probe across 100 temperate European arable-soil samples, benchmarking its default outputs and a simple pH-corrected model against three laboratory reference methods: acid-treated TOC, temperature-differentiated TOC (SoliTOC), and total carbon dry combustion. Uncorrected FarmLab algorithms systematically overestimated SOC by +0.20% to +0.27% (SD = 0.25–0.28%), while pH adjustment reduced bias to +0.11% and tightened precision to SD = 0.23%. Volumetric moisture had no significant effect on measurement error (r = −0.14, p = 0.16). Bland–Altman and Deming regression demonstrated improved agreement after pH correction, but formal equivalence testing (accuracy, precision, concordance) showed that no in-field model fully matched laboratory standards—the pH-corrected variant passed accuracy and concordance evaluation yet failed the precision criterion (p = 0.0087). At ~EUR 3–4 per measurement versus ~EUR 44 for lab analysis, FarmLab facilitates dense spatial sampling. We recommend a hybrid monitoring strategy combining routine, pH-corrected in-field mapping with laboratory-based recalibrations alongside expanded calibration libraries, integrated bulk density measurement, and adaptive machine learning to achieve both high-resolution and certification-grade rigor.