Yurui Sun’s research while affiliated with University of Bonn and other places

A R T I C L E I N F O Keywords: Lactic acid bacteria (LAB) Fermentation dynamics In situ measurement of pH Model-assisted sensor Organic acid Ensiling A B S T R A C T Anaerobic lactobacillus (LAB) fermentation is key to production of silage. Tracking the dynamics of bacterial metabolism is important for the improvement of LAB fermentation and for evaluation of potential additives. This is currently limited by available ex situ analytical methods, while commercial sensors of lactic and acetic acid content remain unavailable. Here we validate an in situ pH sensor and devise a model-sensor package and a mini-fermenter system (1.5 L) for on-line analysis of LAB fermentation. By the fusion of the model based on mirror mapping principle and the time course of pH measured in situ, a general solution of the dynamic accumulation of organic acid (DAOA) and percentage yield of organic acid (PYOA, 0~100 %) is obtained as a function of time. We link PYOA to the initial and final values of lactic and acetic concentrations analyzed ex situ to obtain specific solutions for the time courses of lactic and acetic acid production. We demonstrate the model-sensor system with fermentation of both maize and ryegrass, capturing the dynamic patterns of fermentation, including the rapid depletion of O 2 in the initial aerobic phase (maize: ≈ 1.5 h, ryegrass: ≈ 9.2 h), the exponential decline of pH (R 2 ≥ 0.99, RMSE ≤ 0.043) in the subsequent anaerobic phase, and metabolic feedbacks of pH on instantaneous acid production (ΔpH; inflection at pH 5), and on cumulative yields of total acidity and specific organic acids. These are all previously unavailable data streams.

Silage is produced worldwide for both livestock feeding and biogas production. Sustainable silage production requires characterization and mitigation of potential effects on environmental quality, particularly from greenhouse gas emissions during the production cycle. Ex-situ sampling has demonstrated that major emissions are carbon dioxide (CO2) and ethanol (EtOH). In-situ gas measurements from farm silo and bale silage are rare and may be important to improve our knowledge of the physical and biochemical causes, and constraints on these gas emissions. This study focused on tracking the kinetics of CO2 and EtOH emissions from bale maize silage, with real-time identification, quantification and separation of aerobic and anaerobic respiratory components in the period following opening of the silage. For this, an automatic multi-sensor gas-flux chamber (AMGC) was developed. Three bales (mean weight: 890 kg) of maize silage were tested (n = 3). Oxygen (O2) and temperature (Tsi) sensors were co-located at 10- and 20-cm behind the open face of the bales. Over the two weeks of the experiment we observed: (i) significant initial discharge of CO2 across the open face (1.68-2.55 mol m-2 h-1) and EtOH (0.027-0.034 mol m-2 h-1); (ii) peak CO2 emission occurred when O2 concentration (10 cm depth) was 3∼8% vol., while peak EtOH emission occurred below 2% vol. O2, (iii) dynamic conversion of O2 to CO2 from aerobic respiration; and (iv) the cumulative emission of EtOH during the anaerobic period was 4-6 times greater than that during aerobic plus semi-aerobic periods. These novel measurements provide mechanistic understanding, and may facilitate improved management of silage production to minimize environmental impact and aerobic loss of silage.

The microbiome in silage may vary substantially from the onset to the completion of fermentation. Improved additives and inoculants are being developed to accelerate the ensiling process, to enhance fermentation quality, and to delay spoilage during feed-out. However, current methods for preselecting and characterizing these amendments are time-consuming and costly. Here, we have developed a multi-sensor mini-bioreactor (MSMB) to track microbial fermentation in situ and additionally presented a mathematical model for the optimal assessment among candidate inoculants based on the Bolza equation, a fundamental formula in optimal control theory. Three sensors [pH, CO 2 , and ethanol (EtOH)] provided data for assessment, with four additional sensors (O 2 , gas pressure, temperature, and atmospheric pressure) to monitor/control the fermentation environment. This advanced MSMB is demonstrated with an experimental method for evaluating three typical species of lactic acid bacteria (LAB), Lentilactobacillus buchneri (LB) alone, and LB mixed with Lactiplantibacillus plantarum (LBLP) or with Enterococcus faecium (LBEF), all cultured in De Man, Rogosa, and Sharpe (MRS) broth. The fermentation process was monitored in situ over 48 h with these candidate microbial strains using the MSMB. The experimental results combine acidification characteristics with production of CO 2 and EtOH, optimal assessment of the microbes, analysis of the metabolic sensitivity to pH, and partitioning of the contribution of each species to fermentation. These new data demonstrate that the MSMB associated with the novel rapid data-processing method may expedite development of microbial amendments for silage additives.

The biochemical reactions of aerobic microbial respiration (AMR) suggest that silage temperature (Tsi) rise, oxygen (O2) consumption and carbon dioxide (CO2) emission may be equally useful as indicators of silage deterioration during feed-out, but only temperature has been used extensively to assess aerobic stability. Here we extend the study of aerobic stability to incorporate AMR of silage by developing a novel experimental cell integrated with multiple sensors. Silage samples, ensiled from a triticale crop, were made in twelve air-tight barrels (60 L), packed to bulk densities of 190 or 250 kg m⁻³ dry matter (DM). Tsi and O2 measurements were co-located at 15- and 30-cm behind the working face. CO2 was measured as flux across the working face. The experimental period of aerobic exposure was 7 days. We provide the first reports of: (i) distinct aerobic responses of these parameters, showing that Tsi varied with CO2 in phase but with O2 out-of phase; (ii) CO2 flux was dominated initially by anaerobic discharge and subsequently by aerobic products; (iii) linear relationships between aerobic reheating and both O2 consumption (0.994 ≥ R² ≥ 0.815, P < 0.01) and CO2 flux (0.981 ≥ R² ≥ 0.464, P < 0.01); and (iv) variable magnitude of daily aerobic production of CO2 per kg DM from 2.3 to 133.4 mmol kg d⁻¹. These results demonstrate that the novel multi-sensor technique has powerful capacity to provide insight into AMR of silage and thus provide more detailed information to guide silage management than previous measurements of aerobic stability.

High quality silage containing abundant lactic acid is a critical component of ruminant diets in many parts of the world. Silage deterioration, a result of aerobic metabolism (including utilization of lactic acid) during storage and feed-out, reduces the nutritional quality of the silage, and its acceptance by animals. In this study, we introduce a novel non-disruptive dual-sensor method that provides near real-time information on silage aerobic stability, and demonstrates for the first time that in situ silage temperature (Tsi) and pH are both associated with preservation of lactic acid. Aerobic deterioration was evaluated using two sources of maize silage, one treated with a biological additive, at incubation temperatures of 23 and 33 °C. Results showed a time delay between the rise of Tsi and that of pH following aerobic exposure at both incubation temperatures. A 11 to 25% loss of lactic acid occurred when Tsi reached 2 °C above ambient. In contrast, by the time the silage pH had exceeded its initial value by 0.5 units, over 60% of the lactic acid had been metabolized. Although pH is often used as a primary indicator of aerobic deterioration of maize silage, it is clear that Tsi was a more sensitive early indicator. However, the extent of the pH increase was an effective indicator of advanced spoilage and loss of lactic acid due to aerobic metabolism for maize silage.

Evapotranspiration (ET) plays an important role in water and energy balance at the surface-atmosphere interface. It is widely reported that near-surface soil water content (SWC) or soil water potential (SWP) significantly affects ET and this parameter has been incorporated into the FAO Penman-Monteith (FAO-PM) model for prediction of ET during crop growth seasons. However, there is little information on the effect of SWC or SWP on prediction of ET during soil freeze-thaw cycles in winter. We present an experiment conducted at a demonstration farm with a crop of winter wheat, over two winters near Beijing, China. A lysimeter system equipped with a weather station was used to measure the ET flux and meteorological data. Unfrozen soil water content (USWC) and soil temperature (Tsoil) were measured using dielectric tube sensors (DTS) and digital temperature sensors, respectively. SWP was determined by measured USWC and a soil moisture characteristic (SMC) curve derived from the soil freezing characteristic (SFC) curve and the Clapeyron equation in frozen soil. Detailed measurements in year 1 showed that the FAO-PM model exhibited a complex error pattern, underestimating ET from unfrozen soil but overestimating ET from stable frozen soil. To address these errors, we define a freezing stress index (Ksf) as a function of SWP. Incorporation of Ksf as a modifier of the standard crop coefficient in the FAO-PM model improved prediction of ET (RMSE declined from 0.424 to 0.187 mm day⁻¹, in year 1). These data revealed a correlation between SWP near the soil surface (<10 cm) and measured ET over freezing and thawing cycles. We incorporated a coupled heat and water transfer (CHWT) model into the improved FAO-PM model to predict USWC, SWP and ET throughout the winter of year 2 from current meteorological data as upper boundary conditions, initial measurements of ET, USWC and Tsoil as initial conditions, and Ksf and soil hydraulic properties optimized in year 1. The results showed that ET estimation was significantly improved, with RMSE reduced from 0.323 to 0.281 mm day⁻¹ using the combined (FAOPM-Ksf-CHWT) model. Model error primarily derived from an underestimation of simulated SWP during soil freezing process. The combined model extends the utility of the FAO-PM model to non-cropping seasons including winter in temperate climates and reduces the data requirements for accurate prediction of ET from intermittently frozen soil.

Hydraulic property determination in freezing soils continues to be a substantial challenge. Our overall objective was to present a novel method for in-situ estimation of unsaturated hydraulic conductivity in freezing soil using a combination of improved field data and a simplified inverse modeling approach. Dielectric sensor readings in the field were corrected using knowledge of ice permittivity, temperature and water redistribution to achieve higher accuracy in determination of liquid soil water content and soil ice content. The equation describing unsaturated hydraulic conductivity in freezing soil with an added impedance parameter was inversely estimated by firstly fitting soil freezing and thawing characteristic curves using the measured liquid soil water content and soil temperature, and then minimizing differences of the measured and simulated total soil water content with a coupled heat- and water-transfer model in frozen soil. Field measurements were made over two winters between 2011 and 2013 (year-1: winter of 2011–2012; year-2: winter of 2012–2013) in Beijing, China. Results suggested that (i) the relative error of liquid soil water content measurement in freezing soil was up to 53% if the knowledge of ice permittivity, temperature and water redistribution was neglected; (ii) the estimated impedance parameter in year-1 (1.152) was an order of magnitude higher than in year-2 (0.117), possibly because a faster freezing rate generated more fine ice in year-2, resulting in reduced tortuosity; (iii) the estimated impedance parameter uncertainty likely comes from the model assumptions, the measurement accuracies of model inputs and the inverse modeling parameter estimates. Based on these results and analysis, we conclude that the impedance parameter in frozen soil is strongly related to both the soil ice content and the size of formed ice, which is affected by the freezing rate and by the size of soil pores related to the soil texture.

Carbon dioxide (CO2) is a principal byproduct of various chemical, biological and biochemical processes. CO2 has been measured using various advanced sensors, but a limiting technical challenge has been resolving independent streams of CO2 produced by processes occurring simultaneously. The magnitude and kinetics of each stream may be chemically, biologically or/and physically informative, but such partitioning has received little attention and successful case studies remain rare. In silage production, CO2 flux, an important indicator of aerobic deterioration, microbial activity or oxidative rate of silage, derives from two different pools: gas accumulated in the pores during the early anaerobic phase, and current real-time production of CO2 as oxygenated air enters the silage across the exposed face after opening for feed-out. The former is regarded as a noise confounding the signal and the latter reflects current degradation of the silage. Using a self-developed automatic sensor system with novel signal decomposition, we successfully partitioned CO2 flux into two independent streams derived from the two distinct pools in maize silage, following two independent processes: a physical venting of stored gas through a tortuous diffusive pathway, and a biochemical process generating gas in real time. Three silage samples, treated with a chemical or a biological additive, or left untreated, were tested. The signal decomposition found two best-fit functions (0.8034 ≤ R² ≤ 0.9036), a quadratic CO2 discharge function and an exponential CO2 production function, for characterizing these distinct processes. These results demonstrate chemical sensor with powerful data-processing capability to resolve the complexity of dual-pool CO2 emission.

The HYDRUS-2D simulation software has been used for irrigation management. Its performance under realistic irrigation regimes requires evaluation with new methodologies that integrate larger soil volumes, because soil water content is highly variable in time, space and scale. We compare direct measurements in a sloping field environment under drip irrigation with simulation using HYDRUS-2D. An advanced mobile sensor technology is used to track the dynamics of soil water content, and thus of unsaturated flow, at 0.25 m and 0.50 m depth in a field plot (6 m × 3 m; 4° slope) beneath two parallel (0.5 m separation; 7 emitters per delivery tube) dripper arrays. We document an asynchronous sequence of wetting fronts driven by the sloping surface and capture the field results well using HYDRUS-2D, in the spatial (R² = 0.935–0.963, p < 0.01, RMSE = 0.024–0.027 cm³ cm⁻³) and time (R² = 0.804–0.983, p < 0.01, RMSE = 0.9–1.8 h) domains. In general, the HYDRUS-2D simulation system has potentially excellent capability for characterizing temporal moisture redistribution, tracking wetting front dynamics and predicting the time required for volumetric soil water content (VSWC) to reach field capacity under drip irrigation. Moreover, our study showed that the mobile dielectric sensor is a powerful tool to monitor infiltration of drip array irrigation both in spatial and time domains.

Silage is a critical global feedstock, but is prone to aerobic deterioration. The dominant mechanism of O2 transport into silage remains unresolved. Here, multiple sensors tracked O2 and CO2, gas pressure (ΔP) between internal silage and ambient air, pH and silage temperature (Tsi) during the ensilage of maize and ryegrass. We report the first observation that CO2 produced from microbial respiration was partially dissolved in silage water, with evidence of negative or positive ΔP depending on the changing balance between CO2 production and dissolution. The ΔP < 0 reflected an apparent respiratory quotient (RQ) < 1. Net CO2 production was much greater in anaerobic fermentation stage than in initial aerobic phase or later aerobic feed-out phase. O2 transport into silage is intimately linked to the dynamics of net CO2, ΔP, microbial activity, pH and Tsi. These results suggested that both gas diffusion (based on Fick’s law) and advective transfer (Darcy’s law) play equally important roles in governing the complex temporal progression of inward and outward gas fluxes to and from the silage interior. Even though low pH suppressed microbial activity and supported aerobic stability, the negative ΔP increased the risk of O2 entry and aerobic deterioration during feed-out phase.