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Background: The objective of this study was to evaluate the effects of dietary fatty acids (FA) saturation and lysophospholipids supplementation on growth, meat quality, oxidative stability, FA profiles, and lipid metabolism of finishing beef bulls. Thirty-two Angus bulls (initial body weight: 623 ± 22.6 kg; 21 ± 0.5 months of age) were used. The experiment was a completely randomized block design with a 2 × 2 factorial arrangement of treatments: 2 diets with FA of different degree of unsaturation [high saturated FA diet (HSFA) vs. high unsaturated FA diet (HUFA)] combined with (0.075%, dry matter basis) and without lysophospholipids supplementation. The bulls were fed a high-concentrate diet (forage to concentrate, 15:85) for 104 d including a 14-d adaptation period and a 90-d data and sample collection period. Results: No interactions were observed between dietary FA and lysophospholipids supplementation for growth and meat quality parameters. A greater dietary ratio of unsaturated FA (UFA) to saturated FA (SFA) from 1:2 to 1:1 led to lower DM intake and backfat thickness, but did not affect growth performance and other carcass traits. Compared with HSFA, bulls fed HUFA had greater shear force in Longissimus thoracis (LT) muscle, but had lower intramuscular fat (IMF) content and SOD content in LT muscle. Compared with HUFA, feeding the HSFA diet up-regulated expression of ACC, FAS, PPARγ, and SCD1, but down-regulated expression of CPT1B. Compared with feeding HSFA, the HUFA diet led to greater concentrations of c9-C18:1 and other monounsaturated FA in LT muscle. Feeding HUFA also led to lower plasma concentrations of cholesterol, but there were no interactions between FA and lysophospholipids detected. Feeding lysophospholipids improved growth and feed conversion ratio and altered meat quality by increasing muscle pH24h, redness values (24 h), IMF content, and concentrations of C18:3, C20:5 and total polyunsaturated fatty acids. Furthermore, lysophospholipids supplementation led to lower malondialdehyde content and up-regulated the expression of ACC, FAS, and LPL in LT muscle. Conclusions: Results indicated that supplementing a high-concentrate diet with lysophospholipids to beef bulls can enhance growth rate, feed efficiency, meat quality, and beneficial FA. Increasing the dietary ratio of UFA to SFA reduced DM intake and backfat thickness without compromising growth, suggesting potential improvements in feed efficiency. [ABSTRACT FROM AUTHOR]

Offline land surface models (LSMs) require atmospheric forcing data sets for simulating water, energy, and biogeochemical fluxes. However, available forcing data sets remain highly uncertain and can introduce additional differences in LSM simulations. This study explored the impact of various forcing data sets, ranging from widely used to newly developed, on hydrological simulations using the Common Land Model 2024 (CoLM2024). We conducted 12 global experiments using different forcing data sets to force CoLM2024. We evaluated the model's performance against plot‐scale observations and globally gridded reference data. We examined the uncertainties in forcings and their impact on output variables such as latent heat, sensible heat, net radiation, and total runoff. Globally, precipitation has the highest degree of uncertainty at 4.4%. The forcing uncertainties propagate to the model simulations and cause significant differences in simulated variables. Runoff uncertainty is about 15.7% globally, with a greater impact in low latitudes. Our evaluation shows that the newly developed data sets, such as CRUJRA and ERA5LAND, generally outperform the others. However, the optimal forcing data set varies depending on the variable of interest and the targeted region. Partial Least Squares Regression analysis reveals that different simulated variables are associated with dominant forcing variables, highlighting the importance of selecting forcing data sets for specific applications and regions. This study confirms the importance of improving the quality and consistency of meteorological data. This would help reduce simulation biases and guide the improvement of the model structure and parameterization of CoLM2024. Plain Language Summary: Atmospheric forcing data sets, including precipitation, air temperature, humidity, radiation, wind speed, and air pressure, are necessary for driving offline land surface models, for simulating water, energy, and carbon fluxes. However, atmospheric forcings deviate from each other and can lead to varying results in land surface model (LSM) simulations. Here we investigated to what extent the forcing data set can affect the simulation using the CoLM2024. To achieve this, we compared 12 different forcing data sets, from widely used to newly developed. Our results confirm that the uncertainties among these data sets, particularly in precipitation, can lead to significant differences in simulations such as runoff and surface fluxes. Our results show that the uncertainty in runoff is about 15.7% globally, with more pronounced in tropical regions. We find that using CRUJRA and ERA5LAND data sets improved CoLM2024 simulations compared to traditional ones. Our analysis reveals that different output variables are sensitive to different forcing variables, and the best data set varies depending on the region and the simulated variable. For example, runoff is mainly influenced by precipitation, while net radiation is more sensitive to shortwave radiation. This study underscores the need for improved meteorological data quality to enhance simulation accuracy, guiding CoLM2024 refinement and reducing biases. Key Points: 12 forcing data sets show large uncertainties and great effects on land surface model simulationsNewly data sets for CoLM2024 simulations generally outperformed others, but optimal choice variesPartial Least Squares Regression revealed the mechanism of uncertainty propagation [ABSTRACT FROM AUTHOR]

Estimates of the van Genuchten (1980, abbreviated as VG) parameters and saturated hydraulic conductivity (Ks) were made for the contiguous United States at a resolution of 100 m and seven soil depths by combining the SoilGrids+ (SG+) soil property maps of Ramcharan et al. with the R3H3 member of the Rosetta3 hierarchical pedotransfer functions (PTFs) of Zhang et al. To this end, we developed multi‐threaded code that significantly speeds up computation (up to a factor 25) depending on the level of parallelism. We verified estimates first by calculating simple summary statistics of estimated basic properties of SG+ with actual measured soil properties for 14,113 pedons in the National Cooperative Soil Survey (NCSS) (2023) labsample database. Next, we computed summary statistics of PTF‐estimated moisture contents for NCSS and SG+ data. The results show estimation errors are dominated by intrinsic errors of the PTF, and that (potentially correctable) systematic errors are present in SG+ soil properties and PTF estimates. The resulting hydraulic property maps contain well over 750 million points for each of the seven layers and show considerable horizontal and depth variation for each VG parameter and Ks, except the VG "n" parameter, which is dominated by values between 1.25 and 1.6. The hydraulic property maps are 99.9% complete, and we demonstrate that plausible profiles and uncertainty information can be generated for virtually each point. The maps are available as two multi‐channel GeoTIFF maps per SG+ layer: one with the five hydraulic parameters and one with the corresponding covariances. Core Ideas: van Genuchten parameters and saturated hydraulic conductivity were estimated for the contiguous United States and seven depths.Retention estimates were verified with National Cooperative Soil Survey soil data and analyzed for systematic errors.Parallelized code was developed to speedup future repeat analyses.Uncertainty in hydraulic characteristics can be expressed individually for every point.Data available as GeoTIFF files at 100‐m resolution, two files (parameters and covariances) for each depth. Plain Language Summary: Soil hydraulic properties are needed to calculate the infiltration of water into soils and to estimate flooding potential. Such properties can be measured in the laboratory which is usually an expensive and labor‐intensive task. Pedoytransfer functions are statistical methods that can estimate soil hydraulic properties from primary soil properties such as soil texture and bulk density. Recently, maps of primary properties were published for the contiguous United States at a resolution of 100 m. The present work applies artificial neural network pedotransfer functions to these maps and evaluates the accuracy of the estimates by soil depth and other criteria. The results are encouraging with some suggestions for future improvements. The developed code and estimated data are publicly available. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Background: C16:0 and cis-9 C18:1 may have different effects on animal growth and health due to unique metabolism in vivo. This study was investigated to explore the different effects of altering the ratio of C16:0 and cis-9 C18:1 in fat supplements on growth performance, lipid metabolism, intestinal barrier, cecal microbiota, and inflammation in fattening bulls. Thirty finishing Angus bulls (626 ± 69 kg, 21 ± 0.5 months) were divided into 3 treatments according to the randomized block design: (1) control diet without additional fat (CON), (2) CON + 2.5% palmitic acid calcium salt (PA, 90% C16:0), and (3) CON + 2.5% mixed fatty acid calcium salt (MA, 60% C16:0 + 30% cis-9 C18:1). The experiment lasted for 104 d, after which all the bulls were slaughtered and sampled for analysis. Results: MA tended to reduce 0–52 d dry matter intake compared to PA (DMI, P = 0.052). Compared with CON and MA, PA significantly increased 0–52 d average daily gain (ADG, P = 0.027). PA tended to improve the 0–52 d feed conversion rate compared with CON (FCR, P = 0.088). Both PA and MA had no significant effect on 52–104 days of DMI, ADG and FCR (P > 0.05). PA tended to improve plasma triglycerides compared with MA (P = 0.077), significantly increased plasma cholesterol (P = 0.002) and tended to improve subcutaneous adipose weight (P = 0.066) when compared with CON and MA. Both PA and MA increased visceral adipose weight compared with CON (P = 0.021). Only PA increased the colonization of Rikenellaceae, Ruminococcus and Proteobacteria in the cecum, and MA increased Akkermansia abundance (P < 0.05). Compared with CON, both PA and MA down-regulated the mRNA expression of Claudin-1 in the jejunum (P < 0.001), increased plasma diamine oxidase (DAO, P < 0.001) and lipopolysaccharide (LPS, P = 0.045). Compared with CON and MA, PA down-regulated the ZO-1 in the jejunum (P < 0.001) and increased plasma LPS-binding protein (LBP, P < 0.001). Compared with CON, only PA down-regulated the Occludin in the jejunum (P = 0.013). Compared with CON, PA and MA significantly up-regulated the expression of TLR-4 and NF-κB in the visceral adipose (P < 0.001) and increased plasma IL-6 (P < 0.001). Compared with CON, only PA up-regulated the TNF-α in the visceral adipose (P = 0.01). Compared with CON and MA, PA up-regulated IL-6 in the visceral adipose (P < 0.001), increased plasma TNF-α (P < 0.001), and reduced the IgG content in plasma (P = 0.035). Compared with CON, PA and MA increased C16:0 in subcutaneous fat and longissimus dorsi muscle (P < 0.05), while more C16:0 was also deposited by extension and desaturation into C18:0 and cis-9 C18:1. However, neither PA nor MA affected the content of cis-9 C18:1 in longissimus dorsi muscle compared with CON (P > 0.05). Conclusions: MA containing 30% cis-9 C18:1 reduced the risk of high C16:0 dietary fat induced subcutaneous fat obesity, adipose tissue and systemic low-grade inflammation by accelerating fatty acid oxidative utilization, improving colonization of Akkermansia, reducing intestinal barrier damage, and down-regulating NF-κB activation. [ABSTRACT FROM AUTHOR]

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Northeast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally. Plain Language Summary: Northeast China (NEC) is essential for achieving food accessibility and food security in China, but its agricultural production is seriously threatened by climate change. This study investigated how climate change could impact maize, rice, and soybean crop yields in this region from 2015 to 2100, using the PCSE crop model and forcing from the international Coupled Model Intercomparison Project 6 (CMIP6) climate projections. Simulation results suggested rising temperatures would negatively affect crop yields, especially for maize. Increased carbon dioxide and rainfall partially offset these losses. By 2100, average declines were projected for maize (−21%) and soybeans (−4%), respectively. Altering planting dates to match crop needs was an effective adaptation to boost yields, with rice benefiting the most. Relocating rice production eastward and expanding its area substantially increased simulated rice yields (+62%) but decreased other crops due to competition (−32% maize, −28% soybeans). Even with adaptations, some areas still showed yield declines, indicating extra measures may be needed to maintain production. Overall, this study suggests significant risks to crop yields from climate change in Northeast China, with adaptations like optimized planting and strategic crop relocation able to mitigate some but not all of the projected impacts. Key Points: Rising temperatures negatively impacted simulated future crop yields, especially for maizeAdjusting planting dates boosted yields for rice and soybeans but had limited benefits for maizeRelocating and expanding the rice area increased yields substantially [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Simple Summary: Our objective is to establish an in vitro rumen fermentation model that can dynamically simulate the fermentation process of various total mixed ration (TMR) diets in the rumen of dairy cows, enabling a quantitative investigation of rumen methane and rumen acetic acid concentrations. The models were assessed for their prediction accuracy and precision, while the independent verification experiments confirmed the models' generalization ability across different total mixed ration (TMR) ratios (C:F). These results show that in vitro rumen models constructed by machine learning methods can be used as a tool to quantify rumen fermentation parameters (methane and acetic acid) and guide the dietary structure optimization of dairy cows. The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency. [ABSTRACT FROM AUTHOR]

Little information exists on the variation in morphological characteristics, nutritional value, ruminal degradability, and molecular structural makeup of diverse whole-plant silage corn (WPSC) cultivars among different growing regions. This study investigated the between-regions (Beijing, Urumchi, Cangzhou, Liaoyuan, Tianjin) discrepancies in five widely used WPSC cultivars in China (FKBN, YQ889, YQ23, DK301 and ZD958), in terms of 1) morphological characteristics; 2) crude protein (CP) chemical profile; 3) Cornell Net Carbohydrate and Protein System (CNCPS) CP subfractions; 4) in situ CP degradation kinetics; and 5) CP molecular structures. Our results revealed significant growing region and WPSC cultivar interaction for all estimated morphological characteristics (P < 0.001), CP chemical profile (P < 0.001), CNCPS subfractions (P < 0.001) and CP molecular structural features (P < 0.05). Except ear weight (P = 0.18), all measured morphological characteristics varied among different growing regions (P < 0.001). Besides, WPSC cultivars planted in different areas had remarkably different CP chemical profiles and CNCPS subfractions (P < 0.001). All spectral parameters of protein primary structure of WPSC differed (P < 0.05) due to the growing regions, except amide II area (P = 0.28). Finally, the area ratio of amide I to II was negatively correlated with the contents of soluble CP (δ = -0.66; P = 0.002), CP (δ = -0.61; P = 0.006), non-protein nitrogen (δ = -0.56; P = 0.004) and acid detergent insoluble CP (δ = -0.43; P = 0.008), in conjunction with a positive correlation with moderately degradable CP (PB1; δ = 0.58; P = 0.01). In conclusion, the cultivar of DK301 exhibited high and stable CP content. The WPSC planted in Beijing showed high CP, SCP and NPN. The low rumen degradable protein of WPSC was observed in Urumchi. Meanwhile, above changes in protein profiles and digestibility were strongly connected with the ratio of amide I and amide II. [ABSTRACT FROM AUTHOR]

Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges. [ABSTRACT FROM AUTHOR]

Despite recent developments in geoscientific (e.g., physics- or data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine-learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling–Gupta efficiency, and root-mean-squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and nonlinear behaviors in nature. [ABSTRACT FROM AUTHOR]

Increasing evidence indicates that microscale heterogeneity is critical to interpret and predict macroscopic soil processes and functions. However, the difficulty of measuring soil characteristics, such as soil organic carbon (SOC) and microbial biomass, at the micron level greatly hinders our understanding of how microscale soil heterogeneity quantitatively regulates macroscopic soil behaviors. Here, we investigated the effect of microscale distributions of soil water saturation (S), SOC content (CSOC), and microbial biomass (CMB) on soil heterotrophic respiration (SHR) using a microscale process-based model. The microscale distributions of S, CSOC, and CMB were mathematically determined by assuming they varied linearly with local soil porosity, which was derived from X-ray computed tomography, and the effect of microscale heterogeneity on SHR rates was examined under different water saturations and carbon availabilities. The results show that microscale soil heterogeneity stimulated macroscopic SHR only when it alleviated resource limits or environmental stress. For instance, the heterogeneous S improved the SHR rate by reducing microbial water stress or enhancing dissolved organic carbon (DOC) diffusion, whereas the heterogeneous CSOC enhanced CO2 flux by increasing DOC availability. In addition, the heterogeneous CMB promoted CO2 flux by increasing microbial accessibility of substrates. The interactions among water, SOC, and microbes at the micron scale may stimulate or restrict CO2 emission, depending on soil water saturation and DOC availability. Given the nature of soil heterogeneity at the micron scale and the challenge of measuring microscale soil characteristics, this study provided invaluable insights into how microscale heterogeneity regulates macroscale soil behaviors. [ABSTRACT FROM AUTHOR]

The interplay of surface and shallow subsurface fluxes plays a critical role in controlling water movement in hillslope agroecosystems and impacting soil and plant health during prolonged dry periods, demonstrating a need for in-field monitoring. This study was conducted for two years (2021–2022) by combining field monitoring of the grass-covered inter-row area (passive wick lysimeter, surface runoff, and meteorological data), laboratory determination of soil hydraulic properties (SHPs), and numerical modeling with the aim to explore near-surface fluxes at the SUPREHILL Critical Zone Observatory (CZO) located on a hillslope vineyard. Additionally, sensitivity analysis for basic root water uptake (RWU) parameters was conducted. The model was evaluated (R2, RMSE, and NSE) with lysimeter (hillslope) and runoff (footslope) data, producing good agreement, but only after the inverse optimization of laboratory estimated hydraulic conductivity was conducted, demonstrating that adequate parameterization is required to capture the hydropedological response of erosion-affected soil systems. Results exhibit the dependence of runoff generation on hydraulic conductivity, rainfall, and soil moisture conditions. The data suggest different soil-rewetting scenarios based on temporal rainfall variability. Sensitivity analysis demonstrated that Leaf Area Index (LAI) was the most responsive parameter determining the RWU. The study offers an approach for the investigation of fluxes in the topsoil for similar sites and/or crops (and covers), presenting the methodology of self-constructed soil–water collection instruments. [ABSTRACT FROM AUTHOR]

Simple Summary: Fermented total mixed ration (FTMR) is an effective method of preserving high-moisture byproducts. However, few studies have investigated the effect of feeding FTMR on the rumen microbiota of dairy cows. This study aimed to determine changes and interactions of ruminal microbiota and chemical parameters in dairy cows fed FTMR. Compared with unfermented total mixed ration (TMR), FTMR improved bacterial and fungal richness and diversity. Meanwhile, the ruminal acetate and NH3-N concentration were increased. The prediction of the metabolic function of the flora showed that FTMR could increase lipid and amino acid metabolism. This study aimed to determine changes and interactions of ruminal microbiota and chemical parameters in dairy cows fed FTMR. Twelve multiparous Holstein dairy cows (Body weight = 616 ± 13.4 kg; day in milk = 106 ± 7.55 d; and parity = 2.31 ± 0.49; mean ± standard deviation) were divided randomly into two treatments depending on the day in milk, milk production, and parity. The two treatments were: (1) total mixed ration (TMR) and (2) FTMR. Illumina MiSeq sequencing was used to explore the changes in the ruminal microbiota. The results revealed that the bacterial and fungal diversity of the FTMR group were significantly higher than the TMR group. The predominant microbiota phyla in the bacteria and fungi showed significant differences between TMR and FTMR, as follows: Verrucomicrobia (p = 0.03) and Tenericutes (p = 0.01), Ascomycota (p = 0.04) and Basidiomycota (p = 0.04). The dominant bacterial genera in the bacteria, fungi, protozoan, and archaea that showed significant differences between TMR and FTMR were Unclassified_Bacteroidales (p = 0.02), Unclassified_RFP12 (p = 0.03), Candida (p = 0.0005), Bullera (p = 0.002), Cryptococcus (p = 0.007), and Ostracodinium (p = 0.01). LefSe analysis was performed to reveal the biomarker genera of the rumen microbiota community (bacteria, fungi, protozoan, and archaea) in the TMR and FTMR were the genera Shuttleworthia, Ruminococcus, Cryptococcus, Mycosphaerella, Bullera, Candida, and Ostracodinium. NH3-N concentration (p < 0.0001), total VFA concentration (p = 0.003), and molar proportion in total VFA of acetate (p = 0.01) were higher for the cows fed FTMR compared with the cows fed the TMR. Several bacterial genera showed significant correlations with rumen fermentation parameters. The genus Unclassified_Bacteroidales and Bullera were positively correlated with total volatile fatty acids (VFA) and acetate, whereas Candida and Ostracodinium showed negative correlations. Meanwhile, propionate was positively correlated with Candida and negatively correlated with Bullera. The PICRUSt functional profile prediction indicated that the xenobiotics biodegradation and metabolism, the lipid, amino acid, terpenoids, and polyketides metabolisms of the FTMR group were significantly higher than that of the TMR group. The results imply that FTMR can increase lipid and amino acid metabolism, and modulate the rumen microbiome and improve ruminal fermentation. [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Background: Unreasonable use of antibiotics in animals is a major concern and will remain so, thus affecting people's health. However, the application of plant extracts can better solve this problem. Objectives: The purpose of this study was to study the effect of Moringa leaf flavonoids on the production performance, immunity, and rumen fermentation of dairy cows. Methods: Nine Holstein multiparous cows (average weight: 550 kg; days of lactation: 150 ± 6 days) were used in the experiment, using a 3 × 3 Latin square design. Cows were divided into three groups, each of which was supplemented with 0, 50, or 100 mg/body weight (BW) Moringa oleifera leaf flavonoids. Each experimental period consisted of three periods of 21 days, and the prefeeding period lasted 15 days. Results: Our results indicated that supplementation with Moringa leaf flavonoids increased the protein content and decreased the number of somatic cells in milk; had little effect on the biochemical indices of blood, the rumen fermentation, and serum biochemical indicators; and improved the activity of antioxidant enzymes, the antioxidant capacity, and immunity. Conclusions: Addition of 50 mg/BW Moringa leaf flavonoids to cow enhanced the antioxidant and immunity capacity in dairy cows but did not affect physiological levels of common biochemical parameters in blood or fermentation parameters in rumen. [ABSTRACT FROM AUTHOR]

Simple Summary: Methane and volatile fatty acids are important products of rumen fermentation in dairy cows. Quantitative research on them is of great significance for environmental protection and animal production. The aim of this study was to develop a prediction model using the stacking ensemble learning method and predict the production of rumen fermentation products based on the nutrient level of the diet. The results show that the stacking model has good performance in terms of prediction accuracy. The model proposed in this study can be used as a guideline to optimize diet compositions and improve feeding efficiency. Volatile fatty acids (VFAs) and methane are the main products of rumen fermentation. Quantitative studies of rumen fermentation parameters can be performed using in vitro techniques and machine learning methods. The currently proposed models suffer from poor generalization ability due to the small number of samples. In this study, a prediction model for rumen fermentation parameters (methane, acetic acid (AA), and propionic acid (PA)) of dairy cows is established using the stacking ensemble learning method and in vitro techniques. Four factors related to the nutrient level of total mixed rations (TMRs) are selected as inputs to the model: neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM). The comparison of the prediction results of the stacking model and base learners shows that the stacking ensemble learning method has better prediction results for rumen methane (coefficient of determination ( R 2 ) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA ( R 2 = 0.888, RMSE = 1.975 mmol/L) and PA ( R 2 = 0.924, RMSE = 0.74 mmol/L). And the stacking model simulates the variation of methane and VFAs in relation to the dietary fiber content. To demonstrate the robustness of the model in the case of small samples, an independent validation experiment was conducted. The stacking model successfully simulated the transition of rumen fermentation type and the change of methane content under different concentrate-to-forage (C:F) ratios of TMR. These results suggest that the rumen fermentation parameter prediction model can be used as a decision-making basis for the optimization of dairy cow diet compositions, rapid screening of methane emission reduction, feed beneficial to dairy cow health, and improvement of feed utilization. [ABSTRACT FROM AUTHOR]

Despite recent developments in geoscientific (e.g., physics/data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling-Gupta efficiency, and root mean squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and non-linear behaviors in nature. [ABSTRACT FROM AUTHOR]

Simple Summary: At present, the price of conventional feed ingredients in the world fluctuates greatly, and the economic outcomes of many farms are facing this challenge. At the same time, with the relaxation of the planting restriction policy, the industrial hemp planting area has increased, and the processing of industrial hemp byproducts has become a great challenge. In this study, we compared the chemical composition, carbohydrate and protein composition, in situ ruminal degradability, intestinal digestibility, and available energy values of industrial hemp byproducts and conventional feeds to make up for the gaps in the data of industrial hemp byproducts and provide theoretical support for the application of industrial hemp byproducts in dairy cattle production. The objective of this experiment was to explore the nutritional components of industrial hemp byproducts (industrial hemp ethanol extraction byproduct, IHEEB; industrial hemp stalk, IHS; industrial hemp seed meal, IHSM; industrial hemp oil filter residue, IHOFR) and provide theoretical support for the application of industrial hemp byproducts in dairy cattle production. This experiment used a combination of a wet chemical method with Cornell Net Carbohydrate and Protein System, in situ nylon bag technique, and three-step in vitro method to compare the chemical composition, carbohydrate and protein composition, in situ ruminal degradability and intestinal digestibility of industrial hemp byproducts and conventional feeds (alfalfa hay, AH; soybean meal, SBM). Available energy values were estimated based on the National Academies of Sciences, Engineering, and Medicine. The results showed that the nutritional composition of different feeds varied greatly. The two types of IHEEB were enriched with ash, crude protein (CP), neutral detergent fiber (NDF), and calcium, while the contents of neutral detergent insoluble crude protein, acid detergent insoluble crude protein, and acid detergent lignin were higher. As a result, the non-degradable carbohydrate and protein components were higher, and the effective degradation rate of rumen dry matter and protein was lower. IHS contains higher non-protein nitrogen and NDF, which enables it to provide more CP rumen effective degradation rate and carbohydrates, but the high acid detergent fiber also limits its application. IHSM possesses 296 g/kg CP and high rumen undegradable protein and intestinal digested protein, which can provide rumen bypass protein in dairy cows, making it a potentially good protein source. IHOFR had higher ether extract, rumen available protein degradation rate, and total tract digested protein, which can provide more energy and easily degradable protein for lactating cows. The available energy value of IHEEB and IHS was lower than AH, while SBM is between IHFOR and IHSM. In addition, the tetrahydrocannabinol of three industrial hemp byproducts that have not been assessed by the European Food Safety Authority (EFSA) was tested to evaluate their safety, and all of them were less than the limit set by ESFA. In conclusion, industrial hemp byproducts can be considered for inclusion in dietary formulations as unconventional feed sources for dairy cattle, but the purpose of use needs to be properly considered. [ABSTRACT FROM AUTHOR]

Current databases of soil hydraulic properties (SHPs) have typically been used to develop pedotransfer functions (PTFs) to estimate water retention [θ(h)] assuming a unimodal pore‐size distribution. However, natural soils often show the presence of bimodal to multimodal pore‐size distributions. Here, we used three widely spread databases for PTF development: UNsaturated SOil hydraulic DAtabase (UNSODA) 2.0, Vereecken, and European hydropedological data inventory (EU‐HYDI), to analyze the presence of structural effects in both θ(h) and hydraulic conductivity [K(h)]. Only undisturbed samples were included in the analysis that contained enough datapoints for both θ(h) and K(h) properties, especially in the wet range. One‐hundred ninety‐two samples were suitable for our analysis, which is only 1% of the total samples in the three databases. Results showed that 65% of the samples exhibited a bimodal pore‐size distribution, and bimodality was not limited to fine‐textured but also coarser‐textured soils. The Mualem–van Genuchten (MvG) expression for both unimodal and bimodal soils was not able to predict the observed unsaturated K. Only a joint fitting of measured θ(h) and K(h) functions provided parameter estimates that were able to describe unsaturated K for uni‐ and bimodal soils. In addition, we observed a negative relationship between α and n in the case of low sand content (<52%) for both unimodal and bimodal matrix domain properties, contradicting the classical notion. The ratio of α for the macropore and matrix domain was positively correlated with the fraction of macropores and sand content. We anticipate that the results will contribute to deriving PTF for structured soils and avoid unrealistic combinations of MvG parameters. Core Ideas: Only 1% of the samples in the three databases was suitable for analyzing the presence of bimodality.Bimodality was seen in 65% of the samples, both in fine‐textured and coarser‐textured soils.Joint fitting of θ(h) and K(h) data outperforms the fitting of only θ(h) and using MvG function to estimate K(h).Contradicting the classical notion, a negative relationship between the shape parameters α and n was observed for both uni‐ and bimodal properties.α for macropore and matrix domains was positively correlated with the fraction of macropores and sand content. [ABSTRACT FROM AUTHOR]

Experiments were conducted to evaluate the effects of an aflatoxin B1 (AFB1)-contaminated diet treated with ammonia on the diet detoxification and growth performance, nutrient digestibility, nitrogen utilization, and blood metabolites in sheep. Twenty-four female mutton sheep with an initial body weight of 50 ± 2.5 kg were randomly assigned to one of three groups: (1) control diet (C); (2) aflatoxin diet (T; control diet supplemented with 75 μg of AFB1/kg of dry matter); and (3) ammoniated diet (AT; ammoniated aflatoxin diet). The results showed decreases (p < 0.05) in average daily feed intake, nutrient digestibility of dry matter, crude protein and ether extract, and retained nitrogen, and an increase (p < 0.05) in urine nitrogen excretion in sheep fed diet T compared with those fed the other diets. In comparison to C and AT, feeding T decreased (p < 0.05) the concentrations of total protein, immunoglobulin A, immunoglobulin G, immunoglobulin M, superoxide dismutase, and total antioxidants and increased (p < 0.05) the concentrations of alanine amino transferase, malondialdehyde, and interleukin-6. In summary, ammonia treatment has the potential to decrease the concentration of AFB1 and alleviate the adverse effects of AFB1. [ABSTRACT FROM AUTHOR]

The planting area and total output of pumpkin seeds in China rank first in the world [[1]]; therefore, China produces a large number of byproducts of seed-used pumpkin (seed-used pumpkin flesh, pumpkin seed coat, and pumpkin seed cake) every year. Among these, seed-used pumpkin flesh had the highest PA content, which was significantly higher than that of pumpkin seed cake and pumpkin seed coat ( I p i < 0.05), indicating that seed-used pumpkin flesh can be rapidly degraded in the rumen. In addition, the peak height and peak area of the amide I band, the peak height and peak area of the amide II band, and the peak height and ratio of -helices and -sheets of pumpkin seed cake were significantly higher than those of pumpkin seed coat and seed-used pumpkin flesh ( I p i < 0.05), indicating that the nutritional value of pumpkin seed cake protein is high. In this experiment, the peak height and peak area of amide I and the peak height and peak area of amide II in pumpkin seed cake were significantly higher than those of pumpkin seed coat and seed-used pumpkin flesh, indicating that the nutritional value of protein in pumpkin seed cake is higher. [Extracted from the article]

Fatty acid composition is a key factor affecting the flavor and quality of goat milk. CircRNAs are now recognized as important regulators of transcription, and they play an important role in the control of fatty acid synthesis. Thus, understanding the regulatory mechanisms controlling this process in ruminant mammary glands is of great significance. In the present study, mammary tissue from dairy goats during early lactation and the dry period (nonlactating) were collected and used for high-throughput sequencing. Compared to levels during the dry period, the expression level of circ003429 during early lactation was lower (12.68-fold downregulated). In isolated goat mammary epithelial cells, circ003429 inhibited the synthesis of triglycerides (TAG) and decreased the content of unsaturated fatty acids (C16:1, C18:1, and C18:2), indicating that this circRNA plays an important role in regulating lipid synthesis. A binding site for miR-199a-3p in the circ003429 sequence was detected, and a dual-luciferase reporter system revealed that circ003429 targets miR-199a-3p. Overexpression of circ003429 (pcDNA-circ003429) downregulated the abundance of miR-199a-3p. In contrast, overexpression of miR-199a-3p increased TAG content and decreased mRNA abundance of Yes-associated protein 1 (YAP1) (a target gene of miR-199a-3p), and TAG content was decreased and mRNA abundance was increased in response to overexpression of circ003429. These results indicate that circ003429 alleviates the inhibitory effect of miR-199a-3p on the mRNA abundance of YAP1 by binding miR-199a-3p, resulting in subsequent regulation of the synthesis of TAG and unsaturated fatty acids. [ABSTRACT FROM AUTHOR]

The purpose of this experiment was to investigate the changes of carbohydrate composition in fermented total mixed diet and its effects on rumen fermentation, methane production, and rumen microbiome in vitro. The concentrate-to-forage ratio of the total mixed ration (TMR) was 4:6, and TMR was ensiled with lactic acid bacteria and fibrolytic enzymes. The results showed that different TMRs had different carbohydrate compositions and subfractions, fermentation characteristics, and bacterial community diversity. After fermentation, the fermented total mixed ration (FTMR) group had lower contents of neutral detergent fiber, acid detergent fiber, starch, non-fibrous carbohydrates, and carbohydrates. In addition, lactic acid content and relative abundance of Lactobacillus in the FTMR group were higher. Compared with the TMR group, the in vitro ammonia nitrogen and total volatile fatty acid concentrations and the molar proportion of propionate and butyrate were increased in the FTMR group. However, the ruminal pH, molar proportion of acetate, and methane production were significantly decreased in the FTMR group. Notably, we found that the relative abundance of ruminal bacteria was higher in FTMR than in TMR samples, including Prevotella , Coprococcus , and Oscillospira. At the same time, we found that the diversity of methanogens in the FTMR group was lower than that in the TMR group. The relative abundance of Methanobrevibacter significantly decreased, while the relative abundances of Methanoplanus and vadinCA11 increased. The relative abundances of Entodinium and Pichia significantly decreased in the FTMR group compared with the TMR group. These results suggest that FTMR can be used as an environmentally cleaner technology in animal farming due to its ability to improve ruminal fermentation, modulate the rumen microbiome, and reduce methane emissions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Diacylglycerols as a fat substitute in meat products is a growing area of interest. This study was conducted to analyze the digestion rate, digestion extent, and changes in interfacial properties of lard, glycerolized lard (GL), and purified GL (PGL) in an emulsions system by pH‐stat in vitro digestion. RESULTS: PGL had significantly higher hydrolysis rate and final digestion extent than lard (P ≤ 0.05) during in vitro digestion. The analysis on diameter variations of lard, GL, and PGL during digestion indicated that the surface‑ and volume‐weighted mean particle diameters of all samples had the same variation trend, but variation degree was different. Concurrently, the ζ‐potential analysis of the lard, GL, and PGL during the digestion process showed that the absolute values of the ζ‐potentials of the three types of lipids increased at first and subsequently decreased. The microstructure changes results for lard, GL, and PGL showed there was no obvious flocculation, and the particle size of lard throughout the digestion process was larger than that of GL and PGL. CONCLUSION: This study showed that lard‐based diacylglycerols had high digestibility characteristics and could be applied as a functional lipid in meat products to improve human health. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Bovine mammary epithelial cells (BMECs) of high‐producing dairy cows are subject to constant oxidative stress as a result of high metabolic rate and physiological adaptation to intensive farming. Moringa (Moringa oleifera) leaf has been proposed to have the antioxidant potential in scavenging free radicals due to the presence of flavonoids. In this study, we investigated the cytoprotective effects of moringa leaf flavonoids in alleviating oxidative stress in BMECs in vitro. Oxidative stress was established by exposing isolated BMECs to H2O2 for 2 hr. Doses of moringa leaf flavonoids were evaluated by treating BMECs for 12 hr. The optimal concentrations of H2O2 and moringa leaf flavonoids were 500 μmol/L and 1.0 mg/ml, respectively. The results showed that moringa leaf flavonoids increased the activities of superoxide dismutase, catalase, and glutathione peroxidase; and reduced malondialdehyde activity and intracellular accumulation of reactive oxygen species (ROS) in the H2O2‐induced oxidative stress system. A Hoechst33258 staining assay revealed that moringa leaf flavonoids decreased the apoptosis rate in BMECs, while leaving membrane integrity and nucleolar morphology unchanged. We concluded that moringa leaf flavonoids have the antioxidant capacity to effectively reduce oxidative stress in BMECs. [ABSTRACT FROM AUTHOR]

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a key role in linking water, energy, and carbon cycles. However, at regional scales, variations and controls of ET are still not well understood, due to the substantial uncertainties in ET estimates. Here, the variations in ET over China during the last three decades and their drivers and controls were analysed at monthly, seasonal, and annual scales, using well‐evaluated land surface model simulations. The results suggested that (a) the spatially averaged ET over China increased during 1979–2012 at each temporal scale. Overall, the ET significantly increased (p <.05) in most of western China and decreased in several regions in eastern China during most months and seasons. (a) The increases in spatially averaged ET during the nongrowing season were mainly determined by warming. The increased annual ET, seasonal ET, and monthly ET within the growing season were jointly caused by vegetation greening and climate warming. (c) During November–March, ET was primarily determined by temperature in most of central China and was determined by water availability in the remaining regions. The annual ET and the monthly ET within April–October were mainly controlled by water availability in most of northern and western China, and several regions in central China, by solar radiation in most of central and southern China and by temperature in some regions in the Tibetan Plateau. The study highlights the large effects of warming and greening on ET over China during the last three decades. The results may benefit water resource management under climate change. [ABSTRACT FROM AUTHOR]

BACKGROUND: Yeast products showed beneficial effects with respect to stabilizing ruminal pH, stimulating ruminal fermentation and improving production efficiency. Batch cultures were conducted to evaluate the effects of yeast products on gas production (GP), dry matter disappearance (DMD) and fermentation characteristics of high‐forage substrate. The study was a two media pH (5.8 and 6.5) × five yeasts (three live yeasts, LY: LY1, LY2, LY3; two yeast derivatives, YD: YD4, YD5) × four dosages factorial arrangement, with monensin (Mon) assigned as a positive control. RESULTS: Greater (P < 0.01) GP, DMD, volatile fatty acid (VFA) concentration, ratio of acetate to propionate (A:P) and copy numbers of Fibrobacter succinogenes and Ruminococcus flavefaciens were observed at pH 6.5 than at pH 5.8. The GP kinetics, DMD, VFA concentration, A:P and NH3‐N concentration differed (P < 0.05) among yeasts but varied with media pH or yeast dosages. Increasing doses of LY3 linearly increased DMD (P < 0.04) and VFA concentration (P < 0.001) at media pH 5.8. The DMD linearly (P < 0.02) increased with increased addition of YD4 (pH 6.5) and YD5 (pH 5.8) and the ratio of A:P linearly decreased (P < 0.01) with the addition of YD4 or YD5 at pH 5.8. Overall greater (P < 0.05) GP, A:P (pH 5.8) and DMD (pH 6.5) were observed with yeast products than with Mon. CONCLUSION: LY3 appeared to be an interesting candidate for improving rumen digestibility and fermentation efficiency, particularly at low media pH. YD4 or YD5 improved fermentation efficiency and can be potentially fed as an alternative to Mon. © 2019 Her Majesty the Queen in Right of Canada Journal of the Science of Food and Agriculture © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Modeling land surface processes requires complete and reliable soil property information to understand soil hydraulic and heat dynamics and related processes, but currently, there is no data set of soil hydraulic and thermal parameters that can meet this demand for global use. In this study, we propose a fitting approach to obtain the optimal soil water retention parameters from ensemble pedotransfer functions (PTFs), which are evaluated using the global coverage National Cooperative Soil Survey Characterization Database and show better performance for global applications than our original ensemble estimations (median values of PTFs) as done in Dai et al. (2013, https://doi.org/10.1175/JHM‐D‐12‐0149.1). Soil hydraulic conductivities are still estimated as the median values of multiple PTFs, and the results are shown to perform comparably to the estimates from the existing precision‐advanced models. Soil thermal properties are estimated following the schemes identified by Dai et al. (2019a, http://arxiv.org/abs/1908.04579), which evaluated several highly recommended schemes based on their land modeling applications. Using these approaches, we develop two global high‐resolution data sets of soil hydraulic and thermal parameters based on Global Soil Dataset for Earth System Models (GSDE) and SoilGrids soil composition databases. The delivered variables include six basic soil properties, four soil hydraulic parameters in the Campbell (1974, https://doi.org/10.1097/00010694‐197406000‐00001) model, five soil hydraulic parameters in the van Genuchten (1980, https://doi.org/10.2136/sssaj1980.03615995004400050002x) model, and four soil thermal properties. The delivered data sets are available at a 30″ × 30″ geographical spatial resolution and provide four sets of vertical profiles following the resolutions of SoilGrids, Noah‐Land Surface Models (LSM), Joint UK Land Environment Simulator (JULES), and Common Land Model/Community Land Model (CoLM/CLM). The data sets can be used in both regional and global applications. Plain Language Summary: The parameters of soil hydraulic and thermal properties are essential to the modeling of land surface processes, but no data set is currently available for global use. Using some newly proposed and existing widely used schemes, we developed two global high‐resolution data sets of soil hydraulic and thermal parameters based on two commonly used soil composition databases (Global Soil Dataset for Earth System Models [GSDE] and SoilGrids). The delivered variables include multiple basic soil properties and all the required parameters for solving soil hydraulic and heat dynamics and related processes. The data sets follow the vertical profiles of SoilGrids data set and four land surface models (i.e., Noah‐Land Surface Models [LSM], Joint UK Land Environment Simulator [JULES], Common Land Model [CoLM], and Community Land Model [CLM]). The data sets can be used in both regional and global applications. Key Points: A new fitting approach is proposed to obtain the optimal soil water retention parameters from ensemble pedotransfer functionsThe adopted approaches for soil hydraulic parameter estimations are evaluated to be superior or comparative to other approachesGlobal maps of soil hydraulic and thermal parameters for use in land models are produced based on two soil composition data sets [ABSTRACT FROM AUTHOR]

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The objectives of this study were to investigate the effects of different forage-to-concentrate ratios and sampling times on the genetic diversity of carbohydrate-active enzymes (CAZymes) and the taxonomic profile of rumen microbial communities in dairy cows. Six ruminally cannulated Holstein cows were arbitrarily divided into groups fed high-forage (HF) or low-forage (LF) diets. The results showed that, for glycoside hydrolase (GH) families, there were greater differences based on dietary forage-to-concentrate ratio than sampling time. The HF treatment group at 4 h after feeding (AF4h) had the most microbial diversity. Genes that encode GHs had the highest number of CAZymes, and accounted for 57.33% and 56.48% of all CAZymes in the HF and LF treatments, respectively. The majority of GH family genes encode oligosaccharide-degrading enzymes, and GH2, GH3, and GH43 were synthesized by a variety of different genera. Notably, we found that GH3 was higher in HF than LF diet samples, and mainly produced by Prevotella , Bacteroides , and unclassified reads. Most predicted cellulase enzymes were encoded by GH5 (the BF0h group under HF treatment was highest) and GH95 (the BF0h group under LF treatment was highest), and were primarily derived from Bacteroides , Butyrivibrio , and Fibrobacter. Approximately 67.5% (GH28) and 65.5% (GH53) of the putative hemicellulases in LF and HF treatments, respectively. GH28 under LF treatment was more abundant than under HF treatment, and was mainly produced by Ruminococcus , Prevotella , and Bacteroides. This study revealed that HF-fed cows had increased microbial diversity of CAZyme producers, which encode enzymes that efficiently degrade plant cell wall polysaccharides in the cow rumen. [ABSTRACT FROM AUTHOR]

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 km2 global 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Preferential flow is common in clay or expansive clay soils, involving water bypassing a large portion of the soil matrix. Dye tracer experiment and numerical modeling are used to simulate the surface runoff and subsurface preferential flow patterns influenced by the soil fracture network of a relatively steep hillslope system (slope angle equals to 10 degrees). The result of the experiments indicates that part of the water is infiltrated through cracks, leading to the delay of the initial runoff-yielding time and reduction of the discharge of the surface runoff. The soil water flow is mainly in the matrix when the intensity of precipitation is low. With the increasing of precipitation, soil water movement may become in the form of preferential flow through cracks. In addition, the nonuniformity of soil water infiltration and the depth of the average water infiltration increase as the precipitation intensity increases. To this end, the complete coupling model was established by using the surface-matrix-crack (SMC) model to simulate water flow within discrete fracture as well as to simulate water flow in the soil matrix based on the concept of dual permeability using the traditional Richards’ equation. In this model, the “cubic law” of fluid motion in cracks within smooth parallel plates and the two-dimensional diffusion wave approximation to Saint-Venant equations with momentum term ignored (two-dimensional shallow water equations) were used. The model divides soil water infiltration into two forms and uses the overall method to calculate the exchange of water between the crack networks and matrix regions as well as the exchange water between surface runoff and infiltration water. Results indicate that the SMC model has better performance compared with the traditional equivalent continuum model when those models are used to simulate the surface runoff movement and the soil water movement in the presence of cracks. [ABSTRACT FROM AUTHOR]

Abstract: 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 (ny forward runs). Although employment of the adjoint method can reduce the cost to nd forward 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 nkl order, and it calculates the directional sensitivities (in the directions of nkl eigenvectors) 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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

BACKGROUND Little attention has been paid on the inherent molecular structural effects among agricultural by-products. In this study, soybean meal ( SM), wheat bran ( WB), corn distillers dried grains with soluble ( DDGS), dry brewer's grain ( DBG), wet brewer's grain ( WBG), and apple pomace ( AP), which are widely used in the animal industry were selected to explore protein and carbohydrate molecular structural conformations. RESULTS All the protein peak heights (including α-helix and β-sheet) and areas were exhibited highest values in SM and lowest in AP. The SM had the highest peak area intensity of cellulosic compounds ( CELC), while the remaining varieties showed the lowest absorbance level. The TSCHO (sum of structural carbohydrate ( SCHO) and CELC area exhibited variations among the samples. Multivariate comparisons showed AP had no molecular structural association with other by-products within the protein amide region. Protein amides I, II and (I+ II) areas, α-helix, β-sheet and area ratio of protein amide and ( TSCHO + TCHO) had strong relationships with CP, NDF, ADF, ADL, SCP, starch, PC, CA, CC and TDN contents. CONCLUSION Inherent molecular structures varied among the selected by-product types and they might be used as potential predictors of nutritive factors, especially for protein structural information. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

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 posteriori through 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 m2 down 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. [ABSTRACT FROM AUTHOR]

Simple Summary: Using dietary non-protein nitrogen is an effective way to decrease the dependence on protein resources in cattle production. N-carbamylglutamate (NCG) is a structural analogue of N-acetylglutamate (NAG), which is a precursor of endogenous Arg synthesis. NCG improves urea cycling and enhances the endogenous synthesis of Arg, nitric oxide synthase and NO. The present study showed that beef benefited from being fed NCG product in the urea diet by enhancing its growth and slaughter performance, meat quality, nitrogen metabolism and plasma amino acids. The objectives of this experiment were to investigate the effects of N-carbamylglutamate (NCG) on growth and slaughter performance, meat quality, nitrogen utilization, plasma antioxidant and amino acids of Holstein bulls. In this case, 24 Holstein bulls (490 ± 29.0 kg of body weights and 540 ± 6.1 d of age) were blocked by body weights and age and randomly assigned to 1 of 4 groups: (1) CON group: bulls were fed the control diet, (2) CON + NCG group: bulls were fed the control diet with 40 mg/kg BW NCG, (3) Urea group: bulls were fed the urea diet, and (4) Urea + NCG group: bulls were fed the urea diet with 40 mg/kg BW NCG. Feeding NCG significantly improved ADG, FCR, DM and CP digestibility, carcass weight, slaughter weight, DOP, eye muscle area, shear force (p = 0.001) and reduced L* of color, drip loss and cooking loss. Concurrently, feeding the urea diet induced a decreased ADG, carcass weight and slaughter weight, DOP, eye muscle area and shear force. NCG decreased contents of fecal N and urinary N, plasma urea in bulls and ammonia but increased N retention and utilization, plasma NO, plasma Arg, Leu, Ile and Tyr. On the other hand, feeding the urea diet increased urinary N, plasma urea and ammonia. Thus the study efficiently demonstrates that beef benefited from being fed a NCG product in the urea diet by enhancing its growth and slaughter performance, meat quality, nitrogen metabolism and plasma amino acids. [ABSTRACT FROM AUTHOR]

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that "blue carbon" plays in global carbon cycle. [ABSTRACT FROM AUTHOR]

Simple Summary: The management of body condition scores of dairy cows during the dry period affects postpartum health. It is therefore important to monitor the body condition scores so as to predict the occurrence of postpartum diseases. In this study, statistical process control technology was used to monitor body condition scores and to explore its effects on nutritional strategies and the development of postpartum subclinical ketosis. The results showed that the statistical process control technology was not a good tool for the diagnosis of postpartum subclinical ketosis, but an appropriate tool for the guidance of nutritional strategies. The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies. [ABSTRACT FROM AUTHOR]