Search

This study evaluated the effects of different proportions of palmitic (C16:0) and oleic (cis-9 C18:1) acids in fat supplements on rumen fermentation, glucose (GLU) and lipid metabolism, antioxidant function, and visceral fat fatty acid (FA) composition in Angus bulls. The design of the experiment was a randomized block design with 3 treatments of 10 animals each. A total of 30 finishing Angus bulls (21 ± 0.5 months) with an initial body weight of 626 ± 69 kg were blocked by weight into 10 blocks, with 3 bulls per block. The bulls in each block were randomly assigned to one of three experimental diets: (1) control diet without additional fat (CON), (2) CON + 2.5% palmitic calcium salt (PA; 90% C16:0), (3) CON + 2.5% mixed FA calcium salts (MA; 60% C16:0 + 30% cis-9 C18:1). Both fat supplements increased C18:0 and cis-9 C18:1 in visceral fat (P < 0.05) and up-regulated the expression of liver FA transport protein 5 (FATP5; P < 0.001). PA increased the insulin concentration (P < 0.001) and aspartate aminotransferase activity (AST; P = 0.030) in bull's blood while reducing the GLU concentration (P = 0.009). PA increased the content of triglycerides (TG; P = 0.014) in the liver, the content of the C16:0 in visceral fat (P = 0.004), and weight gain (P = 0.032), and up-regulated the expression of liver diacylglycerol acyltransferase 2 (DGAT2; P < 0.001) and stearoyl-CoA desaturase 1 (SCD1; P < 0.05). MA increased plasma superoxide dismutase activity (SOD; P = 0.011), reduced the concentration of acetate and total volatile FA (VFA) in rumen fluid (P < 0.05), and tended to increase plasma non-esterified FA (NEFA; P = 0.069) concentrations. Generally, high C16:0 fat supplementation increased weight gain in Angus bulls and triggered the risk of fatty liver, insulin resistance, and reduced antioxidant function. These adverse effects were alleviated by partially replacing C16:0 with cis-9 C18:1.

Animal nutritionists have incessantly worked towards providing livestock with high-quality plant protein feed resources. Soybean meal (SBM) has been an essential and predominantly adopted vegetable protein source in livestock feeding for a long time; however, several SBM antinutrients could potentially impair the animal's performance and growth, limiting its use. Several processing methods have been employed to remove SBM antinutrients, including fermentation with fungal or bacterial microorganisms. According to the literature, fermentation, a traditional food processing method, could improve SBM's nutritional and functional properties, making it more suitable and beneficial to livestock. The current interest in health-promoting functional feed, which can enhance the growth of animals, improve their immune system, and promote physiological benefits more than conventional feed, coupled with the ban on the use of antimicrobial growth promoters, has caused a renewed interest in the use of fermented SBM (FSBM) in livestock diets. This review details the mechanism of SBM fermentation and its impacts on animal health and discusses the recent trend in the application and emerging advantages to livestock while shedding light on the research gap that needs to be critically addressed in future studies. FSBM appears to be a multifunctional high-quality plant protein source for animals. Besides removing soybean antinutrients, beneficial bioactive peptides and digestive enzymes are produced during fermentation, providing probiotics, antioxidants, and immunomodulatory effects. Critical aspects regarding FSBM feeding to animals remain uncharted, such as the duration of fermentation, the influence of feeding on digestive tissue development, choice of microbial strain, and possible environmental impact.

Accurate soil moisture (SM) prediction is critical for understanding hydrological processes. Physics-based (PB) models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes. In addition to PB models, deep learning (DL) models have been widely used in SM predictions recently. However, few pure DL models have notably high success rates due to lacking physical information. Thus, we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions. To this end, we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale (attentionmodel). We further built an ensemble model that combined the advantages of different hybrid schemes (ensemblemodel). We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory (ConvLSTM) model for 1–16 days of SM predictions. The performances of the proposed hybrid models were investigated and compared with two existing hybrid models. The results showed that the attentionmodel could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models. Moreover, the ensemblemodel performed best among all hybrid models at all forecast time scales and different soil conditions. It is highlighted that the ensemblemodel outperformed the pure DL model over 79.5% of in situ stations for 16-day predictions. These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Hyper‐resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirements of hyper‐resolution modeling. Here, we developed a comprehensive downscaling framework based on topography‐adjusted methods and automated machine learning (AutoML). With this framework, a 90 m and hourly atmospheric forcing data set was developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) was then forced with the developed forcing data over two complex terrain regions (the Heihe River Basin and Upper Colorado River Basin). We systematically evaluated the downscaled forcing and the CoLM outputs against both in situ observations and gridded data. The ground‐based validation results suggested consistent improvements for all downscaled forcing variables with mean RMSE improved by 6.362%–95.86%. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model showed comparable or better skills in simulating water and energy fluxes, as verified by in situ validations. The hyper‐resolution simulations provided a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high‐resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory‐based shortwave radiation downscaling models and AutoML‐assisted precipitation downscaling models. These findings emphasized the significance of integrating topography‐based downscaling methods for hillslope‐scale simulations. High‐resolution atmospheric forcing data is required for accurate hyper‐resolution land surface modeling. However, the currently available forcing data is not sufficient due to its coarse spatial resolution. In this study, we developed a downscaling framework that employs physical realism equations and machine learning to account for the impact of topography on atmospheric conditions. We tested the framework using the Heihe River Basin and Upper Colorado River Basin as examples and found that the downscaled forcing provided more realistic representations than those from the coarse‐resolution data. The downscaled forcing‐based hyper‐resolution simulation captured topographic heterogeneity and improved water and energy variable simulations. These findings emphasize the efficacy of the proposed topographic‐based downscaling framework and offer a valuable way to enhance predictions over regions with complex terrain. A downscaling framework for atmospheric forcing based on topography‐adjusted methods and automated machine learning is developedThe downscaled forcing offers better estimates of atmospheric conditions in terms of accuracy, magnitude, and spatial detailed featuresThe CoLM driven by the downscaled forcing significantly improved the simulation of water and energy variables over complex terrain areas A downscaling framework for atmospheric forcing based on topography‐adjusted methods and automated machine learning is developed The downscaled forcing offers better estimates of atmospheric conditions in terms of accuracy, magnitude, and spatial detailed features The CoLM driven by the downscaled forcing significantly improved the simulation of water and energy variables over complex terrain areas

Terrestrial evapotranspiration (ET) is a vital process regulating the terrestrial water balance. However, significant uncertainties persist in global ET estimates. Focusing on the area between 60°, we performed an intercomparison of 90 state‐of‐the‐art ET products from 1980 to 2014. These products were obtained from various sources or methods and were grouped into six categories: remote sensing, reanalysis, land surface models, climate models, machine learning methods, and ensemble estimates. It is shown that global ET magnitudes of categories differ considerably, with averages ranging from 518.4 to 706.3 mm yr−1. Spatial patterns are generally consistent but with significant divergence in tropical rainforests. Global trends are mildly positive or negative (−0.10 to 0.37 mm yr−2) depending on categories but with distinct spatial variability. Evaluation against site measurements reveals various performances across land cover types; the ideal point error values range from 0.45 to 0.83, with wetlands performing the worst and open shrublands the best. Using the three‐cornered hat method, there are spatial differences in ET uncertainty, with lower uncertainty for ensemble estimates, showing less than 15% relative uncertainty in most areas. The best global ET data set varies depending on the intended use and study region. Distinct spatial patterns of controlling factors across categories have been identified, with precipitation driving arid and semi‐arid regions and leaf area index dominating tropical regions. It is suggested to include advancing precipitation inputs, incorporate vegetation dynamics, and employ hybrid modeling in future ET estimates. Constraining estimates using complementary data and robust theoretical frameworks can enhance credibility in ET estimation. Evapotranspiration (ET) is an essential process where water transfers from the land to the atmosphere through evaporation and plant transpiration. However, substantial uncertainties exist across available global ET estimates produced by various models and methods. To date, we created the most comprehensive compilation of over 90 state‐of‐the‐art ET data products, including satellite‐based, land surface model, climate model, reanalysis, machine learning, and ensemble‐based estimates, covering 1980–2014 and ranging between 60°N and 60°S. These estimates show large differences in global ET magnitude, trends, and spatial patterns. We evaluated ET estimates against in situ measurements and found diverging performance across land cover types. We used statistical methods to quantify uncertainties in ET data products and found discrepancies in uncertainty distributions across methods. We also identified key factors driving spatial ET variability in each product category. Our analysis underscores the importance and opportunities for reducing the uncertainty in terrestrial ET and provides valuable insights for improving hydroclimatic predictions. Global ET estimates show large discrepancies in magnitude, trends and spatial patterns across 90 state‐of‐the‐art data setsThere is no single best global ET data set suitable for all applications and locations, the choice depends on the intention and region of studyDominant controls on ET differ across categories spatially; constraining estimates via Budyko and novel hybrid modeling can enhance confidence Global ET estimates show large discrepancies in magnitude, trends and spatial patterns across 90 state‐of‐the‐art data sets There is no single best global ET data set suitable for all applications and locations, the choice depends on the intention and region of study Dominant controls on ET differ across categories spatially; constraining estimates via Budyko and novel hybrid modeling can enhance confidence

This study develops an unstructured mesh generation tool for land surface modeling using a multi‐scale hexagon discrete mesh. The tool can automatically determine the required mesh resolution for different regions based on multi‐objective criteria such as elevation, slope, land cover, and land use. The refined unstructured meshes demonstrate significant enhancement in the representation of spatial heterogeneity. The tool is coupled with the Common Land Model (CoLM) to enable land surface simulations using unstructured meshes. Evaluations focused on runoff, river discharge, and inundation indicate improved model performance compared to traditional structured mesh‐based CoLM simulations under the same computational cost constraints. This tool provides new capabilities for more efficiently capturing localized land surface processes and extreme events. The traditional square mesh cells used in land surface models (LSMs) have limitations in capturing the hydrological processes in regions with spatial solid heterogeneity, for example, mountains and forest‐grass transition zones. This study develops a new tool to generate unstructured mesh with flexible cell shapes and sizes. The tool can automatically adjust the mesh resolution based on the various land surface features. Thus, it can represent better spatial differences in elevation, vegetation, soil, etc. For example, it can use finer meshes for mountains and coarser meshes for desert areas. The new unstructured meshes are coupled with an existing LSM called Common Land Model. Model testing shows improved runoff, river discharge, and flooding simulation compared to traditional square mesh models under the same computation cost. Overall, the new unstructured mesh modeling approach enables more efficient and accurate simulation of localized land processes and extreme events, for example, droughts and floods. It has significance for disaster prevention and environmental studies. An unstructured mesh generation tool for land surface models can automatically refine resolution based on multi‐objective landscape criteriaCoupling refined unstructured meshes with the Common Land Model (CoLM) model improves local hydrological simulation compared to traditional mesh‐based CoLMThe tool enables more efficient and accurate modeling of localized land surface processes and extreme events such as droughts and floods An unstructured mesh generation tool for land surface models can automatically refine resolution based on multi‐objective landscape criteria Coupling refined unstructured meshes with the Common Land Model (CoLM) model improves local hydrological simulation compared to traditional mesh‐based CoLM The tool enables more efficient and accurate modeling of localized land surface processes and extreme events such as droughts and floods

Pedo‐transfer functions (PTFs) relate soil/landscape static properties to a wide range of model inputs (e.g., soil hydraulic parameters) that are essential to soil hydrological modeling. Combining PTFs and hydrological models is a powerful strategy allowing the use of soil/landscape static properties for the generalization of large‐scale modeling. However, since the spatial scales of soil hydraulic parameters required for model inputs and soil/landscape static properties are often not identical, cross‐scale transfer is required, which can be a significant source of errors. Here, we investigate uncertainties in cross‐scale transfer and develop an approach that avoids them. The proposed method uses the convolutional neural network (CNN) as a cross‐scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. The proposed CNN approach is applied under two different estimation strategies to invert the hydraulic parameters of a soil‐water balance model and subsequently the quality of the parameters is assessed. Both synthetical and real‐world results around the conterminous United States indicate that in general the employed end‐to‐end strategy is superior to the two‐step strategy. The CNN‐based integrated model successfully reduces potential errors in cross‐scale transfer and can be applied to other areas lacking information on hydraulic parameters or observations. The proposed method can be extended to improve parameter estimation in earth system models and enhance our understanding of key hydrological processes. Uncertainties in scale conversion and pedo‐transfer function from cross‐scale transfer are systematically investigatedA convolutional neural network that integrates scale conversion and pedo‐transfer functions is proposedOur methods effectively incorporate static properties at different scales and reduce errors in the cross‐scale transfer Uncertainties in scale conversion and pedo‐transfer function from cross‐scale transfer are systematically investigated A convolutional neural network that integrates scale conversion and pedo‐transfer functions is proposed Our methods effectively incorporate static properties at different scales and reduce errors in the cross‐scale transfer

The aim of this study was to determine if feeding Lactobacillus rhamnosus GG (GG, ATCC 53013) to neonatal calves would alter their growth, health, rumen fermentation, and bacterial community composition during the preweaning stage; we hypothesized that it would. Twenty-four male Holstein calves were blocked and randomly assigned to 1 of 2 treatment groups: 1) untreated control (CON), or 2) treated with 1 × 1010 cfu/d of a GG suspension (GG). Calves received GG daily, mixed with the milk replacer in the morning feed for 6 wk. Starter and alfalfa hay intakes, as well as feces and respiratory scores, were recorded daily, while body weight and structures were measured weekly. Blood, rumen fluid, and feces samples were collected, from which relevant indicators were detected. The results showed that the administration of GG significantly increased voluntary starter intake (P = 0.023) and ADG (P = 0.035) of the calves. The fecal score (P = 0.018) was lower and the β-hydroxybutyric concentration in the plasma tended to increase (P = 0.092) in calves treated with GG. The pH of the rumen fluid in calves fed GG was lower (P = 0.007), which might be attributable to the tendency (P = 0.083) for total volatile fatty acids concentration to increase. Administration of GG significantly increased the amylase, protease activity, and the microbial protein concentrations (P = 0.043, P = 0.036, and P = 0.044, respectively) in the rumen fluids. Furthermore, GG treatment altered the dominant bacteria order and relative abundance of the bacteria families in the rumen fluids. The microbial diversity indices were significantly affected by GG administration. In conclusion, the neonatal calves fed GG before weaning increased their voluntary starter intake and growth performance, improved the rumen fermentation, and regulated the pattern to normally increase the propionate and butyrate concentrations. Administration of GG also diversified the bacterial community composition in the rumen, and regulated the balance of rumen and intestinal microorganisms. These results indicated that feeding calves GG were beneficial to the rumen development and early weaning.

As a major contributor to methane production in agriculture, there is a need for a suitable methane inhibitor to reduce ruminant methane emissions and minimize the impact on the climate. This work aimed to explore the influence of cordycepin on rumen fermentation, gas production, microbiome and their metabolites. A total of 0.00, 0.08, 0.16, 0.32, and 0.64 g L-1cordycepin were added into fermentation bottles containing 2g total mixed ration for in vitroruminal fermentation, and then the gas produced and fermentation parameters were measured for each bottle. Samples from the 0.00 and 0.64 g L-1cordycepin addition were selected for 16S rRNA gene sequencing and metabolome analysis. The result of this experiment indicated that the addition of cordycepin could linearly increase the concentration of total volatile fatty acid, ammonia nitrogen, the proportion of propionate, valerate, and isovalerate, and linearly reduce ruminal pH and methane, carbon dioxide, hydrogen and total gas production, as well as the methane proportion, carbon dioxide proportion and proportion of butyrate. In addition, there was a quadratic relationship between hydrogen and cordycepin addition. At the same time, the relative abundance of Succiniclasticum, Prevotella, Rikenellaceae_RC9_gut_group, NK4A214_group, Christensenellaceae_R_7_group, unclassified_F082, Veillonellaceae_UCG_001, Dasytricha, Ophryoscolex, Isotricha, unclassified_Eukaryota, Methanobrevibacter,and Piromycesdecreased significantly after adding the maximum dose of cordycepin. In contrast, the relative abundance of Succinivibrio, unclassified_Succinivibrionaceae, Prevotellaceae_UCG_001, unclassified_Lachnospiraceae, Lachnospira, Succinivibrionaceae_UCG_002, Pseudobutyrivibrio, Entodinium, Polyplastron, unclassified_Methanomethylophilaceae, Methanosphaera,and Candidatus_Methanomethylophilusincreased significantly. Metabolic pathways such as biosynthesis of unsaturated fatty acids and purine metabolism and metabolites such as arachidonic acid, adenine, and 2′-deoxyguanosine were also affected by the addition of cordycepin. Based on this, we conclude that cordycepin is an effective methane emission inhibitor that can change the rumen metabolites and fermentation parameters by influencing the rumen microbiome, thus regulating rumen methane production. This experiment may provide a potential theoretical reference for developing Cordyceps byproduct or additives containing cordycepin as methane inhibitors.

A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global‐scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, https://doi.org/10.1029/93WR02931, 1996, https://doi.org/10.1029/96WR01776), which explicitly assume a lognormal pore size distribution and apply the Young‐Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data‐poor to data‐rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi‐based PTFs outperformed two van Genuchten‐based PTFs calibrated on the same data. The new PTFs were applied to a 1 × 1 km2global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes. We developed a set of hierarchical pedotransfer functions for the semiphysical Kosugi water retention modelAn evaluation using globally representative data demonstrated that the PTFs outperformed PTFs based on the van Genuchten retention modelGlobal maps of hydraulic parameters, derived quantities, and associated uncertainties were produced at 1‐km resolution

Hydraulic tomography (HT) is a recently developed technology for characterizing high‐resolution, site‐specific heterogeneity using hydraulic data (nd) from a series of cross‐hole pumping tests. To properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (ny), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (ny× ny) in these models are prodigious for large‐scale 3‐D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (nyforward runs). Although employment of the adjoint method can reduce the cost to ndforward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced‐Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen‐Loeve Expansion (KLE) truncated to nklorder, and it calculates the directional sensitivities (in the directions of nkleigenvectors) to form the covariance and cross‐covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3‐D transient HT analysis of data from a highly heterogeneous field site. The Reduced‐Order Successive Linear Estimator can solve highly parameterized geostatistical inverse problemApproximation of covariance leads to affordable memory and computational costThe uncertainty of the estimates can be updated every iteration efficiently

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale. For the application of pedotransfer functions in current Earth system models, and specifically for the different fluxes of water, solutes, and gas between soil and atmosphere, subject of the land surface models, recent developments of knowledge are entered in a new generation of pedotransfer functions. Methods for development and evaluation of pedotransfer functions are described in this comprehensive review, and perspectives for future developments in different Earth system science disciplines are presented. Challenges are still present for the application in some extreme environments of the Earth. We argue that a comprehensive set of pedotransfer functions can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Even though methodological challenges are still present for extrapolation and scaling, as outlined, integration and validation in global‐scale models is an achievable goal. Methods for development and evaluation of pedotransfer functions are described, and perspectives in different Earth system science disciplines presentedNovel applications are present for the different fluxes of water, solutes, and gas between soil and atmosphere, subject of the land surface modelsMethodological challenges are still present for extrapolation and scaling, but integration and validation in global‐scale models is an achievable goal

Core IdeasA new model inversion strategy for limited data was proposed and evaluated.The method is robust and can easily be interfaced with other vadose zone models.Parameter sensitivity to uncertainties in the root water uptake mechanism was evaluated. It is often a challenge to simulate soil moisture dynamics accurately when limited data are available about the soil. A forward simulation based on pedotransfer function (PTF) estimates of hydraulic parameters indeed produced severely biased moisture fluxes. This study therefore evaluated a new model inversion strategy which is able to use limited data by optimizing factors that scale PTF estimates. An important feature of this strategy is that the model inversion is performed for all profiles simultaneously, thus ensuring that the hydraulic parameters among the different profiles are adjusted at the same time by changing the scale factors while also ensuring that sufficient data are available to make inversion mathematically possible. We evaluated three main inversion models of different complexity. The simplest model (GW5), in which there were five scale factors with no horizon distinction was the most parsimonious in terms of the corrected Akaike's Information Criterion (AICc). GW10 (with two sets of five scale factors for topsoil and subsoil), and GW30 (six sets) performed less well in terms of AICc, but were able to simulate the mean observed moisture profile much better than GW5. Additional inversions demonstrated that textural variation was important to achieve the best performance, thus indicating that the PTF estimates are a necessary part of the inversion. The GW5 model parameters appeared to be insensitive to assumptions made about total root water uptake and its distribution within the profile. The inversion method is simple and can be interfaced without much difficulty with existing vadose zone models.

The soil water retention curve is the fundamental soil hydraulic property to characterize soil water movement and solute transport. Many efforts have been devoted in the past decades to developing models to describe soil water retention curves. However, most of them are empirical equations or assume that soil pore size distributions conform to a lognormal distribution. Yet, few effects have been undertaken to systematically propose and compare a series of possible alternative probability density functions to describe the sigmoid retention curves with parameters physically explainable. Here, we proposed a family of five soil water retention models based on sigmoid functions with parameters of clear physical implications coinciding with the statistical measures of soil pore size distribution. Compared with the widely used models (i.e., Brooks & Corey, 1964; Kosugi, 1996; van Genuchten, 1980), the proposed models have somewhat improved performances to characterize water retention data for a wide range of soil textures without introducing additional model parameters. Two of the proposed models are capable of characterizing the observed two local extrema in the moisture capacity curves. The associated unsaturated hydraulic conductivity models of the proposed soil water retention models are also derived, which show superior performance in characterizing the observed hydraulic conductivities compared with competing models, especially in macropore regimes. Additionally, we analyzed the parameter‐equivalent conversion between the proposed and the existing models, and a simple linear regression equation can be used to derive the parameters of the proposed models from the existing and other alternative different proposed models. A family of soil water retention models based on sigmoid functions and related relative hydraulic conductivity functions are proposedParameters have statistical implications against pore size distributions and can be converted from existing or alternative new modelsCharacterization of hydraulic properties is improved using new models, especially for macropore properties, without additional parameters A family of soil water retention models based on sigmoid functions and related relative hydraulic conductivity functions are proposed Parameters have statistical implications against pore size distributions and can be converted from existing or alternative new models Characterization of hydraulic properties is improved using new models, especially for macropore properties, without additional parameters

The object of this study was to determine the proper mixing ratio of fresh rice straw to corn steep liquor (CSL) to obtain a high protein content silage feed. The following experimental silages were generated: the control (C1), composed of fresh rice straw without CSL additive, mixed with CSL in the ratios of 4:1 (C4), 3:1 (C3) and 2:1 (C2). Lactic acid bacteria (LAB) inoculant was applied at the rate of 50 mL/kg (fresh basis) of forage to achieve a final application rate of 1 × 106 cfu/g of fresh matter (FM). Duplicate silos for each treatment were opened after 0, 3, 7, 10, 20, 30, 45 and 60 d for microbiological and chemical analysis. The results showed that the addition of CSL significantly increased crude protein (CP) contents, and decreased neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents of treatments after 60 d of ensiling (P< 0.05). The lactic acid contents in C4 and C3 were significantly higher than that in C1 (P< 0.05). In summary, mixing fresh rice straw with CSL at addition levels of 4:1 (C4) and 3:1 (C3) can improve the fermentation quality and nutrient composition of fresh rice straw silage. However, a large proportion of CSL (C3) had a negative impact on the aerobic stability of fresh rice straw.

Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of k-means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within-cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated a posteriorithrough inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m2down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site. Two new ways to parameterize vadose zone hydraulic properties based on soil texture are proposed and analyzed.One of these preserves heterogeneity with only a few adjustable parameters.The two approaches are compared through application to deep vadose zone experimental data.

This study was to determine the fermentation quality of a mixture of corn steep liquor (CSL) (178 g/kg wet basis) and air-dried rice straw (356 g/kg wet basis) after being treated with inoculants of different types of lactic acid bacteria (LAB). The treatments included the addition of no LAB additive (control), which was deionized water; homo-fermentative LAB alone (hoLAB), which was Lactobacillus plantarumalone), and a mixture of homo-fermentative and hetero-fermentative LAB (he + hoLAB), which were L. plantarum, Lactobacillus casei, and Lactobacillus buchneri. The results showed that the inoculation of the mixture of CSL and air-dried rice straw with he + hoLAB significantly increased the concentration of acetic acid and lactic acid compared with the control (P < 0.05). The addition of he + hoLAB effectively inhibited the growth of yeast in the silage. The concentration of total lactic acid bacteria in the he + hoLAB-treated silage was significant higher than those obtained in other groups (P < 0.05). The duration of the aerobic stability of the silages increased from 56 h to >372 h. The control group was the first to spoil, whereas the silage treated with he + hoLAB remained stable throughout the 372 h period of monitoring. The results demonstrated that the he + hoLAB could effectively improve the fermentation quality and aerobic stability of the silage.

Correct quantification of mass and energy exchange processes between land surface and atmosphere requires an accurate description of unsaturated soil hydraulic properties. Soil pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters. Here, 13 PTFs were grouped according to input data requirements and evaluated against a well‐documented database (National Cooperative Soil Survey Characterization [NCSS]) covering the continental United States (87.7% of data) and other regions of the globe (12.3% of data). Weighted ensembles were shown to have improved performance over individual PTFs in terms of evaluation criteria. Validation of moisture content estimated from the ensemble models against observations showed promising results. Global maps of soil water retention data from the ensemble models as well as their uncertainty were provided. Our full 13‐member ensemble model provides more accurate estimates than PTFs that are currently being used in Earth system models, which may, therefore, provide improved water fluxes and reduce uncertainty of the estimations. The availability of soil water retention data is essential for quantifying mass and energy exchange processes at the interface between land surface and atmosphere. In large‐scale applications, soil water retention characteristics usually are estimated with empirical models that, unfortunately, use nonuniform predictors and were developed on subsets of the global distribution of soils. Their reliability for global estimates is often unknown. Using a global database, we developed an ensemble of up to 13 previously published models allowing estimates of soil water retention data under data‐poor to data‐rich conditions. High‐resolution global maps of key points in soil water retention characteristics (and their uncertainties) were produced. These maps suggest that middle and high latitudes in the Northern Hemisphere have larger variability of the estimates. The new model provides more accurate estimates than models currently being used in Earth system models. The performance of 13 popular models for estimating soil water retention was quantified using a data set with global coverageRelative to individual models, weighted multimodel ensembles had improved performance with the best obtained with the full ensembleHigh‐resolution global maps of soil water retention properties suitable for Earth system modeling were produced