A validation of WEPP water quality routines in uniform and nonuniform agricultural hillslopes

Current watershed-scale,nonpoint source pollution models do not represent the processes and impacts of agricultural best management practices on water quality with sufficient detail.A Water Erosion Pre-diction Project-Water Quality(WEPP-WQ)model was recently developed which is capable of simulating nonpoint source pollutant transport in nonuniform hillslope conditions such as those with BMPs.However,WEPP-WQ has not been validated for these conditions,and prior validation work only eval-uated calibrated performance rather than uncalibrated performance,with the latter being most relevant to model applications.This study evaluated uncalibrated and calibrated model performance in two plot-scale,artificial rainfall studies.179 observations were compared to corresponding WEPP-WQ simulations of runoff,sediment yield,and soluble and particulate nutrient forms for both nitrogen and phosphorus.Uncalibrated validation results were mixed for the different field conditions,model configurations,and prediction variables.Nash-Sutcliffe Efficiencies for uncalibrated simulations of uniform conditions were generally greater than 0.6 except for soluble nitrogen predictions which were poor.Simulations of nonuniform conditions were generally'unsatisfactory'except for runoff predictions which were quite good(NSE=0.78).Performance was improved substantially for almost all endpoints with calibration.Some exceptions to this occurred because the objective function for calibration was based on log-space differences so as to more equally-weight calibration of unsaturated conditions that tend to produce lesser runoff volumes and sediment yields.Calibrated results for both uniform and nonuniform condi-tions were generally'satisfactory'or'good'according to widely accepted model performance criteria.

Physico-chemical properties and structure of rice cultivars grown in Heilongjiang Province of China

The relationships between chemical composition,structure and physicochemical properties such as pasting,thermal,and textural properties of 12 temperate rice cultivars grown in Heilongjiang Province,China were investigated in this study.There were significant differences in the content of fat,protein,ash,amylose,damaged starch among 12 cultivars.Brabender Viscograph results showed that gelatinization time,peak viscosity,pasting and peak temperature ranged from 9 min to 15 min,2100 BU to 2500 BU,58.75℃to 72.00℃and 63.75℃to 84.45℃,respectively,except for the waxy one Longjing 9.To,Tp,Tc andΔH were found in the range of 58.34℃to 67.96℃,65.39℃to 75.38℃,78.47℃to 90.69℃and 2.036 J/g to 3.127 J/g respectively.Scanning Electron Micro-photograph results showed that all starch granules were polyhedral with irregular shapes,except Longjing 9 showing signs of the loss of physical integrity.The hardness,adhesiveness,springiness,gumminess,cohesiveness and resilience ranged from 395.87 g to 1161.76 g,-90.842 g·mm to-36.4 g·mm,0.498 to 0.747,78.631 g to 466.837 g,0.199 to 0.569,0.321 to 0.504,respectively.Correlation studies indicated that amylose,damaged starch,protein,fat and ash played a more important role in determining the rice properties of pasting,thermal,crystallinity and texture.

Modeling Water Quality Impacts of Growing Corn, Switchgrass, and <i>Miscanthus</i>on Marginal Soils

The goal of the study was to model water quality impacts of growing perennial grasses on marginal soils. The GLEAMS-NAPRA and RUSLE models were used to simulate long-term surface runoff, percolation, erosion, total phosphorus (TP), and nitrate (NO3-N) losses associated with the production of corn-based bioenergy systems (i.e. conventional tillage corn and corn grain plus stover removal), switchgrass and Miscanthus on three marginal quality soils and one good quality soil in Indiana. Simulations showed that switchgrass and Miscanthus had no effect on annual runoff, but decreased percolation by at least 17%. Results also suggested a potential for reduction in erosion for Miscanthus across the soil types examined when compared to corn-based bioenergy production. The production of switchgrass and Miscanthus did not have significant effects on the simulated TP and NO3-N losses in runoff compared to corn production systems. Nitrates leached from fertilized Miscanthus production were approximately 90% lower than NO3-N leached from the production of fertilized switchgrass and corn systems. Additional studies are needed to better understand the hydrology, erosion and nutrient responses of Miscanthus and switchgrass production to meet bioenergy demands.

Application of SWAT to Assess the Effects of Land Use Change in the Murchison Bay Catchment in Uganda

The Soil and Water Assessment Tool (SWAT) is a versatile model presently used worldwide to evaluate water quality and hydrological concerns under varying land use and environmental conditions. In this study, SWAT was used to simulate streamflow and to estimate sediment yield and nutrients loss from the Murchison Bay catchment as a result of land use changes. The SWAT model was calibrated and validated for streamflow for extended periods. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The model satisfactorily simulated stream discharge from the catchment. The model performance was determined with different statistical methods. The results showed a satisfactory model streamflow simulation performance. The results of runoff and average upland sediment yield estimated from the catchment showed that, both have increased over the period of study. The increasing rate of runoff can lead to severe and frequent flooding, lower water quality and reduce crop yield in the catchment. Therefore, comprehensive water management steps should be taken to reduce surface runoff in the catchment. This is the first time the SWAT model has been used in the Murchison Bay catchment. The results showed that, if all uncertainties are minimised, a well calibrated SWAT model can generate reasonable hydrologic simulation results in relation to land use, which is useful to water and environmental resources managers and policy and decision makers.

GIS and L-THIA Based Analysis on Variations of Non-point Pollution in Nansi Lake Basin,China

Non-point source （NPS） pollution is the main threat to regional water quality, and the estimation of NPS pollution load has become an important task for NPS pollution control in China. Combined with geographical information system （GIS）, the long. term hydrologic impact assessment （L-THIA） model was used to evaluate the temporal.spatial changes of chemical oxygen demand （COD）, total nitrogen （TN） and total phosphorus （TP） in Nansi Lake basin from 2000 to 2010. The results show： 1 ） the estimated COD, TN and TP loads in 2010 are 260017.5, 111607. 7 and 6372.0 t with the relative errors of 2.1%, 2. 0 % and - 8.8 % respectively, and more than 90% concentrated in the raining period from June to September; 2） cultivated land and construction land take up more than 80% of the whole Nansi Lake basin, and the proportions of the three kinds of NPS pollution loads coming from cultivated land and construction land are more than 98%; 3 ） during 2000- 2010, the COD, TN and TP loads increase by 8801. 6, 180.3 and 71.9 t respectively, and become the main impact factors on the water quality of Nansi Lake.

Phosphorus Mass Balance of the Illinois River Watershed in Arkansas and Oklahoma

Water quality degradation in the Illinois River Watershed (IRW) has been linked to excess nutrients, primarily phosphorus (P). A mass balance study was performed on the IRW in Oklahoma and Arkansas to quantify the magnitude of P entering the watershed between the closure of Lake Tenkiller’s dam in 1954 to 2006. Results showed that a substantial mass of P was, and is, being imported into the watershed relative to the mass of P leaving the watershed. The study demonstrated that poultry production has been the major contributor of P imported to the watershed since 1964, and is currently responsible for more than 76% of the net annual P additions to the IRW. Between 1949 and 2002, more than 200,000 tonnes of P was added to the IRW. A P delivery ratio for the IRW was computed from the mass balance results and observed loads indicating that 4% of annual P applied to the IRW landscape reached Lake Tenkiller between 1999 and 2006. Allocation of P loads from 1949 to 2002 to Lake Tenkiller computed with the delivery ratio showed that wastewater treatment plant (WWTP) discharges were responsible for more than 50% of P reaching Lake Tenkiller in 1949, followed by dairy cattle (32% of loads). Poultry contribution to P reaching Lake Tenkiller was 5% in 1949 but increased rapidly to become the largest source in 1969 at approximately 49%, and in 2002 at 54%. Both dairy and WWTP portions of the loads declined from 1949 levels with less than 4% attributable to dairy and less than 34% assigned to WWTP in 2002.

Synthesis of ternary magnetic nanoparticles for enhanced catalytic conversion of biomass-derived methyl levulinate into γ-valerolactone

Conversion of levulinic acid and its esters into versatile y-valerolactone(GVL)is a pivotal and challenging step in biorefineries,limited by high catalyst cost,the use of hydrogen atmosphere,or tedious catalyst preparation and recycling process.Here we have successfully synthesized a ternary magnetic nanoparticle catalyst(Al_(2)O_(3)-ZrO_(2)/Fe_(3)O_(4)(5)),over which biomass-derived methyl levulinate(ML)can be quantitively converted to GVL with an extremely high selectivity of>99%and yield of-98%in the absence of molecular hydrogen.Al_(2)O_(3)-ZrO_(2)/Fe_(3)O_(4)(5)incorporates simultaneously inexpensive alumina and zirconia onto magnetite support by a facile coprecipitation method,giving rise to a core-shell structure,welldistributed acid-base sites,and strong magnetism,as evidenced by the X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS),scanning electron microscopy(SEM),transmission electron microscopy(TEM),high-angle annular dark-field scanning-TEM(HAADF-STEM),SEM-energy dispersive Xray spectroscopy(SEM-EDX),temperature-programmed desorption of ammonia(NH3-TPD),temperature-programmed desorption of carbon dioxide(CO_(2)-TPD),pyridine-adsorption infrared spectra(Py-IR),and vibrating sample magnetometry(VSM).Such characteristics enable it to be highly active and easily recycled by a magnet for at least five cycles with a slight loss of its catalytic activity,avoiding a time-consuming and energy-intensive reactivation process.It is found that there was a synergistic effect among the metal oxides,and the high efficiency and selectivity originating from such synergism are evidenced by kinetic studies.Furthermore,a reaction mechanism regarding the hydrogenation of ML to GVL is proposed by these findings,coupled with gas chromatography-mass spectrometry(GC-MS)analysis.Accordingly,this readily synthesized and recovered magnetic nanocatalyst for conversion of biomassderived ML into GVL can provide an eco-friendly and safe way for biomass valorization.

A WEPp-Water Quality model for simulating nonpoint source pollutants in nonuniform agricultural hillslopes:Model development and sensitivity

The Water Erosion Prediction Project(WEPP)model code was modified extensively to support the simulation of nonpoint source(NPS)pollutant sourcing and transport in nonuniform hillslopes based on NPS science from the Soil and Water Assessment Tool(SWAT).This was accomplished utilizing WEPP's overland flow element(OFE)in place of SWAT's hydrologic response unit(HRU)construct which enabled more physically plausible routing within a hillslope.In addition,several improvements to the NPS code base were implemented.These include:free-source format,modern-Fortran conventions,minor enhancements to NPS model science,and code refactoring.This manuscript documents all model development activities,presents a comparison of relevant WEPP and WEPP-WQ code bases,and performs a local sensitivity analysis of the final model code for the most important input parameters and processes.Sensitivity results indicated that the model performed as expected according to its design and provided important insights for potential subsequent validation studies.

Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

Comprehensive comparison of cellulose nanocrystal (CNC) drying using multi-frequency ultrasonic technology with selected conventional drying technologies

Cellulose nanocrystals(CNCs)have garnered increased attention due to their renewable nature,abundant feedstock availbility,and good mechanical properties.However,one of the bottlenecks for its commercial production is the drying process.Because of the low CNC concentrations in suspension after isolation,CNC drying requires the removal of a large amount of water to obtain dry products for the following utilization and saving shipping costs.A novel multi-frequency,multimode,modulated ultrasonic drying technology was developed for CNC drying to improve product quality,reduce energy consumption,and increase production rate.CNCs dried with dif-ferent drying technologies were characterized by Fourier transform infrared(FT-IR)spectra anal-ysis,X-ray diffraction(XRD)analysis,thermogravimetric analysis(TGA),differential scanning calorimetry(DSC),and redispersibility to measure the quality and property changes.Under the same temperature and airflow rate,ultrasonic drying enhanced drying rates,resulting in at least a 50%reduction in drying time compared to hot air drying.The mean particle sizes of CNC from ultrasonic drying changed little with settling time,indicating good redispersibility.In addition,ultrasonic dried CNCs exhibited good stability in aqueous solutions,with the zeta potentials rang-ing from-35 to-65 mV.Specific energy consumption and CO_(2) emissions of various CNC drying technologies were evaluated.Energy consumption of ultrasonic drying is significantly reduced compared to other drying technologies.Moreover,the potential CO_(2) emissions of the fully elec-trified ultrasonic drying could be net zero if renewable electricity is used.

猪舍环境适宜性模糊综合评价

猪舍环境是影响猪健康水平、生长和繁殖的重要因素,对养猪生产起着决定性作用,受到广泛的关注。然而,猪舍环境是由多个环境因子相互耦合而形成的复杂的非线性时变系统。对各环境因素适宜性的描述并不是一个确定的数值,而是在一定范围内的模糊概念。目前,针对猪舍环境优劣评价大多仅限于单一环境因素,缺少基于多个环境因素的猪舍环境适宜性综合评价研究。因此,该文构建了评价指标体系和权重,根据猪舍养殖环境标准建立各环境因子的隶属度函数,提出了基于模糊集理论的猪舍多环境因子适宜性综合评价方法。以美国普渡大学SERB猪舍环境监测的24组数据为例对该文提出的方法进行了验证,结果表明,该文建立的猪舍环境适宜性模糊综合评价方法比单一环境因素的评价更加全面,能科学合理地反映出猪舍环境质量状况,可以很好地为猪舍环境调控提供数据参考。

密闭式猪舍多环境因子调控系统设计及调控策略

大多数猪舍环境调控是建立在传统控制方法基础上的单一环境变量控制系统,难以对具有多个变量的系统建立精确的数值模型。该文基于模糊控制理论,以温度偏差和温度偏差变化率作为输入量,以通风模式和加热模式为输出控制量建立温度控制器;以相对湿度偏差和氨气浓度偏差为输入量,以通风模式为输出控制量建立通风控制器;并对不同季节多环境因子进行模糊化及逻辑推理,生成不同季节的调控策略及规则,建立2个具有双输入变量的非线性控制系统,加入动态补偿控制,优化猪舍环境调控系统。该文以在美国普渡大学环境研究猪舍监测所得的数据对建立的方法进行了模拟验证。结果表明,舍内温度与设定值最大相对误差为5%,实现了舍内温度稳定控制;舍内相对湿度与设定值最大相对误差为6.3%,充分满足湿度控制要求;猪舍氨气浓度变化范围为2.0~3.7 mg/m^3,远远小于设定值9.1 mg/m^3。因此,该文提出的猪舍多环境因子模糊控制系统及策略,能够很好地满足猪舍环境控制要求,为解决寒冷冬季猪舍温度与通风调控提供可行的思路。

基于能质平衡的密闭猪舍内小气候环境模拟与验证

良好的猪舍内小气候可以显著提高猪的生长性能和健康水平,然而由于猪舍内小气候受地域、季节、饲养数量等因素影响,难以实现可靠的预测及控制。该文基于能量及质量平衡方程,建立热量、湿度交换模型,以实际监测数据为基础,利用多元非线性回归方法(multiple nonlinear regression method)确定模型中的部分参数,建立适用于北方夏季密闭式猪舍环境模拟模型。对夏季北向背阴面和南向朝阳面的2个猪舍内温度及湿度进行模拟及验证,结果表明,南北2个朝向的猪舍内温度、湿度模拟与实测值变化趋势一致,温度最大误差为2.4℃,最大相对误差为9.2%,决定系数分别为0.836 9和0.786 9;舍内相对湿度最大误差为13.34%,最大相对误差为49.66%,决定系数分别为0.912和0.899 7。研究结果可为密闭式猪舍内环境调控及能量需求提供参考。

Comparing End-Use Potential for Industrial Food-Waste Sources

Approximately one quarter of the global edible food supply is wasted. The drivers of food waste can occur at any level between production, harvest, distribution, processing, and the consumer. While the drivers vary globally, the industrialized regions of North America, Europe, and Asia share similar situations; in each of these regions the largest loss of food waste occurs with the consumer, at approximately 51% of total waste generated. As a consequence, handling waste falls on municipal solid waste operations. In the United States, food waste constitutes 15% of the solid waste stream by weight, contributes 3.4 -107 t of carbon dioxide （CO2） equivalent emissions, and costs 1.9 billion USD in disposal fees. The levels of carbon, nutrients, and moisture in food waste make bioprocessing into higher value products an attractive method for mitigation. Opportunities include extraction of nutraceuticals and bioactive compounds, or conversion to a variety of volatile acids-including lactic, acetic, and propionic acids-that can be recovered and sold at a profit. The conversion of waste into volatile acids can be paired with bioen- ergy production, including hydrogen or biogas. This present review compares the potential for upgrading industrial food waste to either specialty products or methane. Higher value uses of industrial food waste could alleviate approximately 1.9-108 t of CO2 equivalent emissions. As an example, potato peel could be upgraded to lactic acid via fermentation to recover 5600 million USD per year, or could be converted to methane via anaerobic digestion, resulting in a revenue of 900 million USD per year. The potential value to be recovered is significant, and food-waste valorization will help to close the loop for various food industries.

Methods for measuring soil infiltration:State of the art

Soil infiltration is the process by which water on the soil surface penetrates the soil.Quantifying the soil infiltration capacity(soil infiltrability)is very important for determining components of the hydrological modeling,irrigation design and many other natural or man made processes.In this paper,commonly used methods for soil infiltration rate measurement with their principles and application conditions are introduced.The advantages and disadvantages of each method under various application conditions are discussed for comparison.Three new methods for soil infiltrability measurement,including the corresponding algorithm models,and the experimental apparatus and procedures are introduced.These analyses should facilitate the choice of method used for soil infiltrability measurement.

Effect of cooling pad installation on indoor airflow distribution in a tunnel-ventilated laying-hen house

Extra cooling pads on the sidewalls are needed for larger poultry houses using tunnel ventilation system.Preliminary study showed that the airflow velocity going through different aisles varies greatly when the extra pads are installed at the end of sidewalls,making a“[”-shape air inlet.Combined with field tests,the CFD(computational fluid dynamics)technology was used to study the uniformity of airflow distribution in a tunnel-ventilated laying-hen house.The air distribution was first monitored in a layer house to find the main reason resulting in the variations of airflows in different aisles.Then CFD simulations were carried out with different distances(D=2 m,3 m or 4 m)between the pads on end-wall and the extra pads on side walls.The field test showed that airflow streams from the different groups of cooling pads collided vertically at the house corners,mixed with each other,then flew towards the center of the house.This was the main reason that the wind speed in the middle aisle was much higher than in other aisles,leaving large zones of lower ventilation in the aisles adjacent to the sidewalls.The results of CFD simulations indicated that air distributions could be significantly improved when the extra pieces of pads were moved away for an appropriate distance from the end coolingpads.As far as conventional poultry house with a span of 12 m,the air speeds in different aisles were more uniform when this distance was about 3 m.

Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping

Currently,the presence of genetically modified(GM)organisms in agro-food markets is strictly regulated by enacted legislation worldwide.It is essential to ensure the traceability of these transgenic products for food safety,consumer choice,environmental monitoring,market integrity,and scientific research.However,detecting the existence of GM organisms involves a combination of complex,time-consuming,and labor-intensive techniques requiring high-level professional skills.In this paper,a concise and rapid pipeline method to identify transgenic rice seeds was proposed on the basis of spectral imaging technologies and the deep learning approach.The composition of metabolome across 3 rice seed lines containing the cry1Ab/cry1Ac gene was compared and studied,substantiating the intrinsic variability induced by these GM traits.Results showed that near-infrared and terahertz spectra from different genotypes could reveal the regularity of GM metabolic variation.The established cascade deep learning model divided GM discrimination into 2 phases including variety classification and GM status identification.It could be found that terahertz absorption spectra contained more valuable features and achieved the highest accuracy of 97.04%for variety classification and 99.71%for GM status identification.Moreover,a modified guided backpropagation algorithm was proposed to select the task-specific characteristic wavelengths for further reducing the redundancy of the original spectra.The experimental validation of the cascade discriminant method in conjunction with spectroscopy confirmed its viability,simplicity,and effectiveness as a valuable tool for the detection of GM rice seeds.This approach also demonstrated its great potential in distilling crucial features for expedited transgenic risk assessment.

Experimental study on baling rice straw silage

For resolving harvesting technology of fresh rice straw silage and plugging of the round steel-roll baler in China,experimental researches were carried out.For harvesting technology,baling silage and chopping silage were experimented.For the round baler,three kinds of feeding rolls equipped for enhancing feeding capability were experimented separately by reliability.Experimental results indicate:harvesting technology of baling fresh rice straw as silage is practicable;slicing-disc feeding rolls can be used to the baler to resolve plugging in straw-baling course.And through further experiments by reliability and density,optimal structure of the feeding roll is obtained:big and small slicing-discs arranged in interval and inclination,distance between slicing-discs being 30-40 mm.

Biomimetic glycosaminoglycan-based scaffolds improve skeletal muscle regeneration in a Murine volumetric muscle loss model

Volumetric muscle loss(VML)injuries characterized by critical loss of skeletal muscle tissues result in severe functional impairment.Current treatments involving use of muscle grafts are limited by tissue availability and donor site morbidity.In this study,we designed and synthesized an implantable glycosaminoglycan-based hydrogel system consisting of thiolated hyaluronic acid(HA)and thiolated chondroitin sulfate(CS)cross-linked with poly(ethylene glycol)diacrylate to promote skeletal muscle regeneration of VML injuries in mice.The HA-CS hydrogels were optimized with suitable biophysical properties by fine-tuning degree of thiol group substitution to support C2C12 myoblast proliferation,myogenic differentiation and expression of myogenic markers MyoD,MyoG and MYH8.Furthermore,in vivo studies using a murine quadriceps VML model demonstrated that the HA-CS hydrogels supported integration of implants with the surrounding host tissue and facilitated migration of Pax7+satellite cells,de novo myofiber formation,angiogenesis,and innervation with minimized scar tissue formation during 4-week implantation.The hydrogel-treated and autograft-treated mice showed similar functional improvements in treadmill performance as early as 1-week post-implantation compared to the untreated groups.Taken together,our results demonstrate the promise of HA-CS hydrogels as regenerative engineering matrices to accelerate healing of skeletal muscle injuries.