猪急性腹泻综合征冠状病毒SYBR Green荧光定量PCR检测方法的建立及应用

为建立快速、灵敏且特异的检测猪急性腹泻综合征冠状病毒（swine acute diarrhea syndrome coronavirus,SADS-CoV）检测方法,本试验扩增SADS-CoV N基因保守区域将其克隆至pMD18-T载体。所构建的重组质粒pMD18-T-SADS-qN作为阳性质粒标准品,以其为模板建立一种SYBR Green荧光定量PCR检测方法。结果显示,所建立方法在3.31×10¹~3.31×10⁷拷贝·μL^-1模板量时,呈良好的线性关系,相关系数（R²）为0.997,斜率为-3.318。该方法特异性检测SADS-CoV;而猪流行性腹泻病毒（PEDV）、猪传染性胃肠炎病毒（TGEV）、猪德尔塔冠状病毒（PDCoV）和猪繁殖与呼吸综合征病毒（PRRSV）检测结果均为阴性。所构建的标准品检测灵敏度下限可以达到3.31×10¹拷贝·μL^-1,组内和组间变异系数均小于1%,表明其具有良好的灵敏性和重复性。用该方法检测SADS-CoV感染IPI-2I和IPEC-J2细胞后不同时间点和不同接毒剂量的复制情况,结果显示,SADS-CoV感染细胞后2 h病毒含量较低,在12~36 h病毒含量迅速增长,36 h后增长速度减缓且病毒含量维持在较高水平。分别用0、0.1、1 MOI SADS-CoV感染细胞结果显示病毒的mRAN转录水平呈现剂量依赖性增加,当MOI为1时,IPI-2I和IPEC-J2细胞病毒含量分别为10^6.7、10^5.3拷贝·mL^-1。进一步利用所建立的方法对经口服攻毒SADS-CoV仔猪的临床样本进行检测,结果发现病毒在空肠回肠含量较高,表明病毒主要定殖于空肠和回肠。综上表明,本研究建立SYBR Green荧光定量PCR检测方法能灵敏特异地检测SADS-CoV,为SADS-CoV的诊断和病毒相关基础研究提供可靠的检测手段。     

马铃薯渣饲用研究进展及应用前景分析

马铃薯淀粉渣富含养分、成本低、产量高,是良好的饲料来源。文章分析马铃薯的饲用价值,从青贮和发酵两方面综述马铃薯渣的饲用化研究现状,并提出马铃薯渣饲用潜力开发的主要制约因素,展望其饲用化的应用前景,为马铃薯渣的饲用化研究提供参考。     

培肥措施对旱地农田土壤CO2排放和碳库管理指数的影响

阐明长期有机物料施肥下土壤CO₂排放特征及其影响机制以及碳库管理指数对黄土高原旱作农业区固碳减排及施肥模式选择的影响尤为重要。基于2012年设置在陇中黄土高原旱作区的长期定位试验,通过不施肥（CK）、氮肥（NF）、有机肥（OM）、秸秆（ST）、有机肥结合无机肥（OMNF）5个处理,测定并计算了2018年不同施肥措施下全年土壤CO₂排放、作物碳排放效率和碳库管理指数的变化,并运用结构方程模型分析了0~30 cm土壤温度、水分、微生物量碳氮、易氧化有机碳、蔗糖酶、脲酶与土壤CO₂排放速率的关系。结果表明：1）与不施肥相比,秸秆、有机结合无机肥和有机肥处理使生育期土壤CO₂排放平均速率提高了42.72%、30.82%和29.79%,秸秆、有机肥处理分别使生育期土壤CO₂排放量显著提高36.35%、32.45%（P<0.05）,有机结合无机肥处理使碳排放效率显著降低41.10%（P<0.05）;2）有机物料处理均能显著提高0~5 cm土层易氧化有机碳、微生物量碳氮、蔗糖酶活性和碳库管理指数,相比不施肥和氮肥处理,有机结合无机肥处理分别使0~30 cm土壤碳库管理指数提高127.41%,99.33%（P<0.05）;3）结构方程模型表明,环境因子对土壤CO₂排放速率的总解释度为53%,对土壤CO₂排放速率总效应较大的影响因素包括土壤温度（2.36）、微生物量碳（1.59）和土壤水分（1.18）,且均间接地影响着土壤CO₂排放速率,土壤温度促进了微生物量碳和蔗糖酶活性的提高,微生物量碳促进了微生物量氮和易氧化有机碳的增加。综合来看,有机结合无机肥处理可以提升土壤碳库管理指数,保持微生物活性,增加作物产量,降低土壤碳排放效率,是陇中黄土高原旱作农业区比较适宜的农田培肥措施。     

A proposal to leverage high-quality veterinary diagnostic laboratory large data streams for animal health,public health,and One Health

Test data generated by ~60 accredited member laboratories of the American Association of Veterinary Laboratory Diagnosticians (AAVLD) is of exceptional quality. These data are captured by 1 of 13 laboratory information management systems (LIMSs) developed specifically for veterinary diagnostic laboratories (VDLs). Beginning ~2000, the National Animal Health Laboratory Network (NAHLN) developed an electronic messaging system for LIMS to automatically send standardized data streams for 14 select agents to a national repository. This messaging enables the U.S. Department of Agriculture to track and respond to high-consequence animal disease outbreaks such as highly pathogenic avian influenza. Because of the lack of standardized data collection in the LIMSs used at VDLs, there is, to date, no means of summarizing VDL large data streams for multi-state and national animal health studies or for providing near-real-time tracking for hundreds of other important animal diseases in the United States that are detected routinely by VDLs. Further, VDLs are the only state and federal resources that can provide early detection and identification of endemic and emerging zoonotic diseases. Zoonotic diseases are estimated to be responsible for 2.5 billion cases of human illness and 2.7 million deaths worldwide every year. The economic and health impact of the SARS-CoV-2 pandemic is self-evident. We review here the history and progress of data management in VDLs and discuss ways of seizing unexplored opportunities to advance data leveraging to better serve animal health, public health, and One Health.     

寒冷地区草原有毒有害牧草监测与防治

草原是内蒙古地区草食畜牧养殖产业健康发展的基础,同时也是优质牧草的主要来源。有毒有害牧草生长扩散预示着草场退化,对当地草食畜牧养殖产业健康发展会造成毁灭性打击。有毒有害植物生长发育,不仅会危害牲畜健康且还会严重消耗土壤的水分和养分,妨碍优良牧草繁殖生长,使草场产量和质量显著下降。在长时间草场利用过程中,很多农牧民群众只注重索取,不注重生态环境保护,使草场生产能力和抵抗能力逐渐变差,给有毒有害牧草繁殖生长提供了条件。因此需要加强寒冷地区草原有毒有害牧草的监测,并采取针对性措施进行防控,降低发病率。该文主要探讨内蒙古呼伦贝尔市寒冷地区草原有毒有害牧草的种类,并提出相应的防治措施。     

农机培训在目前市场经济环境下的改革分析

文章主要根据现阶段市场经济条件下农村农机培训工作的发展现状,深入剖析现阶段农机培训的问题,根据农机培训的具体要求,重点分析农机培训工作的改革内容和改革方向,力图通过改进农机培训工作,加强农机培训改革力度,全面提升农业生产力,推动农村经济的发展。     

Estimation of Runoff,Peak Discharge and Sediment Load at the Event Scale in a Medium‐Size Mediterranean Watershed Using the Annagnps Model

In this paper, the Annualized Agricultural Non‐Point Source (AnnAGNPS) model has been used to estimate runoff, peak discharge and sediment load at the event scale in a Mediterranean watershed. The study area is the Carapelle torrent, Southern Italy (area = 506 km²), where continuous rainfall, streamflow and sediment load data are available. Nineteen flood events have been registered in the period 2007–2009 and were used for the application of the model. The aim of the paper is to evaluate the predictive accuracy of the model at the event scale, in a medium‐size watershed, given the specific conditions of the semi‐arid environments. A sensitivity analysis has been carried out to assign the correct parameterization: the mean normalized output variation of the most meaningful input parameters pointed out the influence of the curve number on runoff, peak discharge and sediment load predictions (values greater than 1); the MN Manning's roughness coefficient and K, C and P factors of the universal soil loss equation showed a moderate influence on sediment load simulations (values between 0·5 and 1). The selection of the Soil Conservation Service synthetic storm types has been based on the observed storm events analysis to improve the peak discharge simulations. The model prediction has proved to be good for runoff (R² = 0·74, NSE = 0·75, W = 0·92) and peak discharge (R² = 0·85, NSE = 0·70, W = 0·94), and satisfactory for sediment yield (R² = 0·70, NSE = 0·63, W = 0·91). The relative error is lower for high events; this result is quite interesting in semi‐arid environments, where most of the annual sediment yield is concentrated in a few, severe events. Copyright © 2013 John Wiley & Sons, Ltd.