内蒙古高原紫花苜蓿土壤氮素丰缺指标和推荐施氮量

为了给内蒙古高原紫花苜蓿（Medicago sativa L.）测土施氮奠定科学基础，本研究采用“零散实验数据整合法”和“养分平衡-地力差减法”新应用公式，开展了该自然区域紫花苜蓿土壤氮素丰缺指标和推荐施氮量研究。结果表明：内蒙古高原生长第1年紫花苜蓿土壤碱解氮第1~6级丰缺指标为≥48，20~48，8~20，4~8，2~4和<2 mg·kg^-1，土壤全氮第1~5级丰缺指标为≥1.4，0.8~1.4，0.4~0.8，0.2~0.4和<0.2 g·kg^-1，土壤有机质第1~6级丰缺指标为≥17，10~17，6~10，3~6，2~3和<2 g·kg^-1。当紫花苜蓿目标产量9~18 t·hm^-2、氮肥利用率40%时，内蒙古高原紫花苜蓿第1~6级土壤推荐施氮量分别为0，68~135，135~270，203~405，270~540和338~675 kg·hm^-2。     

The Determination of Soil Boron Fractions,Their Relationships to Soil Properties and the Availability to Olive (Olea europea L.) Trees

This study was conducted to investigate the various boron fractions in olive tree grown soils. The correlations between boron fractions in leaves, fruits and soil properties were examined. For this purpose cv “Gemlik” olive (Olea europaea L.) orchards were visited. Soil samples from 0–30 cm and 30–60 cm deep, the leaf and fruit samples were collected. The greatest proportion of total soil boron is present in residual form (85–88%). It is followed by organically bound B (2.84–4.50%), specifically adsorbed on soil colloid surfaces (0.93–1.31%), oxides (manganese oxyhydroxides, amorphous Fe and Al oxides, crystalline Fe and Al oxides) bound B (7.27–8.31%). The smallest one readily soluble (extracting plant available) boron values were composed of only 0.40–0.50% of total boron ranging. To determine readily soluble boron five different extraction solutions were in the order Hot water ? 0.01 M CaCl₂ ? 1 M NH₄OAc ? 0.1 M KCl ? 0.005 M DTPA. Fruit boron concentration and soil boron fractions showed close correlations than leaf boron concentration.     

基于云课堂的混合式教学法在实践课程教学的研究

随着信息技术的发展,以及传统教学模式下学生学习现状的不容乐观,世界范围内多种新教改模式相继出现。其中以云课堂为基础的混合式教学法,迅速扩展到教育的各个领域。本文研究了云课堂的混合式教学法在我校药物分析与检验技术课程中的实践应用,从研究背景、研究的方法目标及实践教学设计等方面进行了阐述。     

基于高光谱成像技术识别水稻纹枯病

【目的】利用高光谱成像技术对水稻纹枯病进行早期的快速无损识别,结合判别分析方法建立相应的鉴别模型。【方法】以健康和感染纹枯病的水稻幼苗为研究对象,采集叶片和冠层各180个样本的380~1 030 nm波段的360条高光谱图像,剔除明显噪声部分后,以440~943 nm波段作为水稻样本的光谱范围,分别用不同的方法预处理获得水稻叶片的光谱曲线。采用偏最小二乘–判别分析(PLS-DA)对不同预处理的光谱建模。采用MNF算法对冠层的原始光谱数据进行特征信息提取,并基于特征信息建立线性判别分析(LDA)模型和误差反向传播神经网络(BPNN)判别模型。【结果】标准正态变量变换(SNV)预处理后建立的PLS-DA模型的预测集判别正确率最高,为92.1%。基于特征信息的LAD和BPNN模型的判别结果优于基于全波段的PLS-DA判别模型。基于最小噪声分离变换特征信息提取的BPNN模型取得了最优效果,建模集和预测集正确率分别达99.1%和98.4%。【结论】采用高光谱成像技术对水稻纹枯病生理特征进行无损鉴别是可行的,本研究为水稻纹枯病的识别提供了一种新方法。     

Rapid Estimation of Potentially Mineralizable N in Early Spring Following Fall Biosolids Applications to Winter Wheat

The amount of potentially mineralizable nitrogen (PMN) in early spring is critical to winter wheat production because maximum nitrogen (N) uptake begins then. We evaluated the Haney-Brinton CO₂-burst method for quantifying PMN in early spring from biosolids fall-applied to soft red winter wheat (Triticum aestivum L.). Anaerobically digested and lime stabilized biosolids were fall-applied at 0, 50 and 100 kg PAN ha^?1 at six locations in the Virginia Coastal Plain in 2013. The Haney-Brinton CO₂-burst method did not provide an accurate estimation of spring PAN for biosolids applied in the fall for no-till winter wheat. The sum of preplant soil inorganic N and PMN of the unamended soil was better related to grain yield than PMN alone. The Haney-Brinton CO₂-burst method is inadequate for use in no-till management but may provide an accurate mid-season estimate of winter wheat yield potential when used in combination with pre-plant soil inorganic N.     

基于云计算的食品有毒有害物质检验检测大数据的风险分析算法及其应用

基于云计算,结合食品安全检验检测的完备性与最小性原理,将影响食品安全的多维因素降维成平均含量(AVE)、限量标准(STA)、超限率(OUT)、超限程度(OUD)和最大值(MAX)5个因素,并建立食品有毒有害物质检验检测大数据的风险分析算法。利用云计算技术实现对地理上分布广泛、动态、复杂性高的海量数据进行存储,并运用云计算的MapReduce计算框架进行智能的并行数据处理及计算,最后得到风险分析结果。通过对在基于Web端的实验室管理系统采集的1 000 000条检验检测数据结果进行风险分析,得出食品安全指数IFS远小于1,表明消费者人群的食品安全状态良好。     

Recommendations for designing and conducting veterinary clinical pathology biologic variation studies

The recent creation of a veterinary clinical pathology biologic variation website has highlighted the need to provide recommendations for future studies of biologic variation in animals in order to help standardize and improve the quality of published information and to facilitate review and selection of publications as standard references. The following recommendations are provided in the format and order commonly found in veterinary publications. A checklist is provided to aid in planning, implementing, and evaluating veterinary studies on biologic variation (Appendix  S1 ). These recommendations provide a valuable resource for clinicians, laboratorians, and researchers interested in conducting studies of biologic variation and in determining the quality of studies of biologic variation in veterinary laboratory testing.     

中国农牧交错带燕麦土壤有效磷丰缺指标与适宜施磷量初步研究

为了给中国农牧交错带燕麦施肥提供科学依据,采用土壤有效养分含量与缺素处理相对产量回归方程法、土壤有效养分丰缺分级改良方案和"养分平衡—地力差减法"确定适宜施肥量新应用公式,开展了中国农牧交错带燕麦土壤有效磷丰缺指标与适宜施磷量研究。结果表明:中国农牧交错带燕麦土壤有效磷(Olsen-P)第1~8级丰缺指标依次为≥45、27~45、16~27、9~16、5.5~9、3.5~5.5、2.0~3.5、2.0 mg/kg。当磷肥当季利用率20%、目标产量1.5~6.0 t/hm~2时,土壤有效磷丰缺级别第1~8级的适宜施磷量范围依次为0~0、8~30、15~60、23~90、30~120、38~150、45~180、53~210 kg/hm~2。     

Soil available potassium affected by rice straw incorporation and potassium fertilizer application under a rice–oilseed rape rotation system

Data from 147 field trials were collected to study the influence of straw incorporation on soil potassium (K) under an intensive rice–oilseed rape rotation system, while pot experiments were conducted to evaluate the effects of rice straw incorporation on soil K availability. A significant correlation was observed between the soil available K and the relative yield (RRY) and the relative K uptake (RKU) of oilseed rape, with R² values ranging from 0.07 to 0.08 and from 0.10 to 0.11, respectively, when data were fitted to a logarithmic equation model. In approximately 30% of trials, RRY reached 90%, while soil test available K values were below the critical limit, indicating that soil K values at the time of sampling (within 1 week of rice harvest) underestimated the actual soil K supply capacity. The pot experiment results showed that soil available K was affected by straw incorporation and soil type in the fallow period. The NH₄OAc‐K and NaBPh₄‐K concentrations of soils increased at first, and then, plateaued after 28 days. Straw incorporation significantly influenced the critical soil K concentration, which is important for making accurate K fertilizer recommendation. These results suggested that straw K should be seriously considered in making K fertilizer recommendations. Extending the sampling time from 1 to 3 weeks after the harvesting of rice to stabilize the effects of straw incorporation may help achieve a more accurate evaluation of soil available K.