Quantifying microbial growth and carbon use efficiency in dry soil environments via 18 O water vapor equilibration.

Soil microbial physiology controls large fluxes of C to the atmosphere, thus, improving our ability to accurately quantify microbial physiology in soil is essential. However, current methods to determine microbial C metabolism require liquid water addition, which makes it practically impossible to measure microbial physiology in dry soil samples without stimulating microbial growth and respiration (namely, the "Birch effect"). We developed a new method based on in vivo 18 O-water vapor equilibration to minimize soil rewetting effects. This method allows the isotopic labeling of soil water without direct liquid water addition. This was compared to the main current method (direct 18 O-liquid water addition) in moist and air-dry soils. We determined the time kinetics and calculated the average 18 O enrichment of soil water over incubation time, which is necessary to calculate microbial growth from 18 O incorporation in genomic DNA. We tested isotopic equilibration patterns in three natural and six artificially constructed soils covering a wide range of soil texture and soil organic matter content. We then measured microbial growth, respiration and carbon use efficiency (CUE) in three natural soils (either air-dry or moist). The proposed 18 O-vapor equilibration method provided similar results as the current method of liquid 18 O-water addition when used for moist soils. However, when applied to air-dry soils the liquid 18 O-water addition method overestimated growth by up to 250%, respiration by up to 500%, and underestimated CUE by up to 40%. We finally describe the new insights into biogeochemical cycling of C that the new method can help uncover, and we consider a range of questions regarding microbial physiology and its response to global change that can now be addressed.

Inhibition profile of three biological nitrification inhibitors and their response to soil pH modification in two contrasting soils.

Up to 70% of the nitrogen (N) fertilizer applied to agricultural soils is lost through microbially mediated processes, such as nitrification. This can be counteracted by synthetic and biological compounds that inhibit nitrification. However, for many biological nitrification inhibitors (BNIs), the interaction with soil properties, nitrifier specificity, and effective concentrations are unclear. Here, we investigated three synthetic nitrification inhibitors (SNIs) (DCD, DMPP, and nitrapyrin) and three BNIs [methyl 3(4-hydroxyphenyl) propionate (MHPP), methyl 3(4-hydroxyphenyl) acrylate (MHPA), and limonene] in two agricultural soils differing in pH and nitrifier communities. The efficacies of SNIs and BNIs were resilient to short-term pH changes in the neutral pH soil, whereas the efficacy of some BNIs increased by neutralizing the alkaline soil. Among the BNIs, MHPA showed the highest inhibition and was, together with MHPP, identified as a putative AOB/comammox-selective inhibitor. Additionally, MHPA and limonene effectively inhibited nitrification at concentrations comparable to those used for DCD. Moreover, we identified the effective concentrations at which 50% and 80% of inhibition is observed (EC50 and EC80) for the BNIs, and similar EC80 values were observed in both soils. Overall, our results show that these BNIs could potentially serve as effective alternatives to SNIs currently used.

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.

Meta-analysis protocol on the effects of cover crops on pool specific soil organic carbon.

Soil organic carbon (SOC) plays an important role in agricultural soils, as it contributes to overall soil health as well as climate change mitigation and adaptation. By conducting a meta-analysis, we aim to quantitatively summarize research studying the effects of cover crops (CC) on SOC pools throughout soil depths in arable cropland. We included global studies located in the climatic zones present in Europe. The pools chosen for this analysis are the particulate organic carbon (POC) and the mineral associated organic carbon (MAOC) and the microbial biomass carbon (MBC). Alongside, we will study the effects of a broad range of moderators, such as pedo-climatic factors, other agricultural management practices and CC characteristics e.g., type. We identified 71 relevant studies from 61 articles, of which mean values for SOC pools, standard deviations and sample sizes for treatments (CC) and controls (no CC) were extracted. To perform the meta-analysis, an effect size will be calculated for each study, which will then be summarized across studies by using weighing procedure. Consequently, this meta-analysis will provide valuable information on the state of knowledge on SOC pool change influenced by CC, corresponding quantitative summary results and the sources of heterogeneity influencing these results.

Microbial responses to soil cooling might explain increases in microbial biomass in winter.

In temperate, boreal and arctic soil systems, microbial biomass often increases during winter and decreases again in spring. This build-up and release of microbial carbon could potentially lead to a stabilization of soil carbon during winter times. Whether this increase is caused by changes in microbial physiology, in community composition, or by changed substrate allocation within microbes or communities is unclear. In a laboratory incubation study, we looked into microbial respiration and growth, as well as microbial glucose uptake and carbon resource partitioning in response to cooling. Soils taken from a temperate beech forest and temperate cropland system in October 2020, were cooled down from field temperature of 11 °C to 1 °C. We determined microbial growth using 18O-incorporation into DNA after the first two days of cooling and after an acclimation phase of 9 days; in addition, we traced 13C-labelled glucose into microbial biomass, CO2 respired from the soil, and into microbial phospholipid fatty acids (PLFAs). Our results show that the studied soil microbial communities responded strongly to soil cooling. The 18O data showed that growth and cell division were reduced when soils were cooled from 11 to 1 °C. Total respiration was also reduced but glucose uptake and glucose-derived respiration were unchanged. We found that microbes increased the investment of glucose-derived carbon in unsaturated phospholipid fatty acids at colder temperatures. Since unsaturated fatty acids retain fluidity at lower temperatures compared to saturated fatty acids, this could be interpreted as a precaution to reduced temperatures. Together with the maintained glucose uptake and reduced cell division, our findings show an immediate response of soil microorganisms to soil cooling, potentially to prepare for freezing events. The discrepancy between C uptake and cell division could explain previously observed high microbial biomass carbon in temperate soils in winter. Supplementary Information: The online version contains supplementary material available at 10.1007/s10533-023-01050-x.

14C-labeled organic amendments: Characterization in different particle size fractions and humic acids in a long-term field experiment.

Knowledge about the stabilization of organic matter input to soil is essential for understanding the influence of different agricultural practices on turnover characteristics in agricultural soil systems. In this study, soil samples from a long-term field experiment were separated into silt- and clay-sized particles. In 1967, 14C labeled farmyard manure was applied to three different cropping systems: crop rotation, monoculture and permanent bare fallow. Humic acids (HAs) were extracted from silt- and clay-sized fractions and characterized using photometry, mid-infrared and fluorescence spectroscopy. Remaining 14C was determined in size fractions as well as in their extracted HAs. Yields of carbon and remaining 14C in HAs from silt-sized particles and Corg in clay-sized particles decreased significantly in the order: crop rotation > monoculture â‰« bare fallow. Thus, crop rotation not only had the largest overall C-pool in the experiment, but it also best stabilized the added manure. Mid-infrared spectroscopy could distinguish between HAs from different particle size soil fractions. With spectroscopic methods significant differences between the cropping systems were detectable in fewer cases compared to quantitative results of HAs (yields, 14C, Corg and Nt). The trends, however, pointed towards increased humification of HAs from bare fallow systems compared to crop rotation and monoculture as well as of HAs from clay-sized particles compared to silt-sized particles. Our study clearly shows that the largest differences were observed between bare fallow on one hand and monoculture and crop rotation on the other.

Nutritionally relevant elements in staple foods: influence of arable site versus choice of variety.

Eighteen representative sites for the Austrian grain-growing and eight for the potato-growing zones (soils and crops) were investigated. On each site, total element contents (B, Ba, Ca, Cd, Co, Cu, Fe, K, Li, Mg, Mn, Mo, Na, P, Sr and Zn) were determined in 4-12 varieties of winter wheat (n = 136), 6 varieties of spring durum wheat (n = 30), 5 varieties of winter durum wheat (n = 15), 7 varieties of rye (n = 49), 5 varieties of spring barley (n = 30) and 5 varieties of potatoes (n = 40). Element accumulations in grain species and potato tubers varied significantly with site conditions, with the main exceptions for B in potatoes and wheat as well as for Zn, Cu and Co in durum wheat. On average, across all investigated sites, differences in varieties occurred concerning the elements Ca, Cd, Ba, Sr and Zn (except Zn in potatoes and winter durum). A rough estimation revealed that an average Austrian consumer of wheat, rye and potatoes meets more than 50% of the needs of daily element intake for K, P and Mg, between 36 and 72% for Fe, Zn and Cu, and more than 100% for Co, Mo and Mn. In particular, the elements Ca and Na have to be added from other sources.

Impact of different tillage practices on molecular characteristics of humic acids in a long-term field experiment - an application of three different spectroscopic methods.

The present paper describes changes in soil organic carbon (SOC) and extractable humic acids (HAs) in a long-term field experiment with different tillage treatments (minimum tillage (MT), reduced tillage (RT) and conventional tillage (CT)). This field experiment is located in the east of Vienna in a Pannonian climate and it was started in 1988. The methodological approach included elemental analyses, FT-IR, 13C NMR and fluorimetric measurements. Both MT and RT revealed significant depth gradients of yields of extractable HAs. In CT no depth gradient was observable, neither for HA yields nor for observed molecular characteristics. This indicated a destruction of the gradient by mixing of the soil in CT. Especially MT showed an increase of aromatic moieties with depth, suggesting an increased humification of HAs in the lower soil layers. Gradients with similar trends were indicated for the carbonylic, the amidic and probably the hydroxylic groups in HAs extracted from MT and RT samples. The data revealed with FT-IR and solid-state 13C NMR spectroscopy were convincing, plausible and meaningful, the highly sensitive fluorescence spectroscopy was limited because of strong quenching by inner filter effects, compromising data reliability. However, the fluorescence results based on a defined HAs concentration (and comparing soils from the same site) were in line with results from the other methods (13C NMR and FT-IR). As a consequence, the influence of tillage treatments can be followed by absence or presence of depth gradients of the according molecular characteristics in extracted HAs.

Re-evaluation of the yield response to phosphorus fertilization based on meta-analyses of long-term field experiments.

Phosphorus (P) fertilizer recommendations in most European countries are based on plant-available soil P contents and long-term field experiments. Site-specific conditions are often neglected, resulting in excessive P fertilizer applications. P fertilization experiments including relevant site and soil parameters were evaluated in order to analyze the yield response. The database comprises about 2000 datasets from 30 field experiments from Germany and Austria. Statistical evaluations using a classification and regression tree approach, and multiple linear regression analysis indicate that besides plant-available soil P content, soil texture and soil organic matter content have a large influence on the effectiveness of P fertilization. This study methodology can be a basis for modification and specification of existing P fertilization recommendations and thus contribute to mitigate environmental impacts of P fertilization.