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With the increasing concern over the potential pollution from farm wastes, there is a need for rapid and robust methods that can evaluate livestock manure nutrient content. Physicochemical models which related cattle manure nutrient content (ammonium nitrogen, AN; total potassium, TK; total nitrogen, TN; total phosphorus, TP) to its physicochemical properties (specific gravity, dry matter, electrical conductivity, pH) have been reported by previous researchers. This study reviewed previous physicochemical models and compiled the observed data drawn from a wide selection of sources to validate various physicochemical models. Several statistical parameters, including the coefficient of determination R 2, the modelling efficiency statistic, the mean squared error of prediction, the mean bias, and the linear bias, were calculated to evaluate model performance on the data sets. The results showed that the relationship (AN=0.136EC−0.523) developed by , provided satisfactory predictions for AN with R 2 =0.75. The equation (TP=0.01DM+0.057) developed by gave reasonable prediction for TP with R 2 =0.77. The corrected TK model (TK=0.184EC+0.042) greatly reduced systemic errors and hence improved TK prediction with R 2 =0.87. Compared with prediction models for other nutrient contents, TN physicochemical models provided far from satisfactory predictions but equations used by was the best (R 2 =0.62). [Copyright &y& Elsevier]

Prediction models for rapidly determining the nutrient content of pig manures will become important as environmental regulations increase the requirements for this information. Physicochemical models which related pig manure nutrient content (ammonium nitrogen, AN; total potassium, TK; total nitrogen, TN; total phosphorus, TP) to its physicochemical properties (specific gravity, dry matter, electrical conductivity, pH) have been reported by previous researchers. This study reviewed previous physicochemical models and compiled the observed data drawn from a wide selection of sources to validate various physicochemical models. Several statistical parameters, including the coefficient of determination (R 2), the modelling efficiency statistic, the mean squared error of prediction, the mean bias, and the linear bias, were calculated to evaluate model performance on the datasets. The results showed that the relationship developed by Scotford et al. [Scotford I M; Cumby T R; White R P; Carton O T; Lorenz F; Hatterman U, et al. (1998b). Estimation of the nutrient value of agricultural slurries by measurement of physical and chemical properties. Journal of Agricultural Engineering Research, 71(3), 291–305], can provide satisfactory predictions for AN with R 2 =0.89. The equations developed by Scotford et al. [Scotford I M; Cumby T R; White R P; Carton O T; Lorenz F; Hatterman U, et al. (1998b). Estimation of the nutrient value of agricultural slurries by measurement of physical and chemical properties. Journal of Agricultural Engineering Research, 71(3), 291–305] and Zhu et al. [Zhu J; Zhang Z; Ndegwa P M (2003). Using a soil hydrometer to measure the nitrogen and phosphorus contents in pig slurries. Biosystems Engineering, 85(1), 121–128], gave reasonable prediction for TK and TP with R 2 =0.88 and 0.78, respectively. Compared with prediction models for other nutrient contents, TN physicochemical models provided far from satisfactory predictions but equations used by Martinez-Suller et al. [Martinez-Suller L; Azzellino A; Provolo G (2008). Analysis of livestock slurries from farms across northern Italy: relationship between indicators and nutrient content. Biosystems Engineering, 99(4), 540–552], were the best (R 2 =0.73). These findings demonstrated that physicochemical model may be an appropriate tool to predict AN, TK, and TP content of pig manure, however, further work is needed to develop and validate TN physicochemical models. [Copyright &y& Elsevier]

With increasing concern over the potential pollution from farm wastes, there is a need for rapid and robust methods that can analyse animal manure. In order to evaluate rapid testing methods based on the relationship between layer manure composition (ammonium nitrogen, total potassium, total nitrogen, total phosphorus, iron, copper, zinc, magnesium and sodium) and physicochemical properties (specific gravity, electrical conductivity, pH), diverse layer manure samples (n =105) were used. Relationships were investigated using linear regression and artificial neural networks (ANNs). The performance of a neural network-based model was compared with a linear regression-based model using the same observed data. It was found that ANN model consistently gives better predictions. Based on the results of this study, ANNs appear to be a promising technique for predicting layer manure composition. [Copyright &y& Elsevier]

Aerobic composting is a promising technology for converting manure into organic fertilizer with low capital investment and easy operation. However, the large nitrogen losses in conventional aerobic composting impede its development. Interconversion of nitrogen species was studied during membrane-covered aerobic composting (MCAC) and conventional aerobic composting, and solid-, liquid-, and gas-phase nitrogen migration pathways were identified by performing nitrogen balance measurements. During the thermophilic phase, nitrogenous organic matter degradation and therefore NH 3 production were faster during MCAC than uncovered composting. However, the water films inside and outside the membrane decreased NH 3 release by 13.92%–22.91%. The micro-positive pressure environment during MCAC decreased N 2 O production and emission by 20.35%–27.01%. Less leachate was produced and therefore less nitrogen and other pollutants were released during MCAC than uncovered composting. The nitrogen succession patterns during MCAC and uncovered composting were different and NH 4 + storage in organic nitrogen fractions was better facilitated during MCAC than uncovered composting. Overall, MCAC decreased total nitrogen losses by 33.24%–50.07% and effectively decreased environmental pollution and increased the nitrogen content of the produced compost. [Display omitted] • Membrane-covered aerobic composting (MCAC) decreased total N loss by 33.24%–50.07%. • MCAC decreased N 2 O but increased NH 3 production and emissions. • The water film in the MCAC system prevented 13.92%–22.91% of NH 3 emissions. • MCAC promoted conversion of NH 4 + into organic N and therefore decreased N losses. • MCAC significantly decreased leachate production during composting. [ABSTRACT FROM AUTHOR]

Abstract: A novel lectin (AMML) was isolated from a Chinese herb, i.e., the roots of Astragalus mongholicus, using a combination of ammonium sulfate fraction and ion exchange chromatographies. The molecular mass of intact AMML was determined to be 66,396Da by MALDI-TOF mass spectrometry and 61.8kDa by gel filtration, respectively. AMML was a dimeric protein composed of two identical subunits each with a molecular mass of 29.6kDa. The lectin was a glycoprotein with a neutral carbohydrate content of 19.6%. The purified lectin hemagglutinated both rabbit and human erythrocytes, and showed preference for blood types O (native) and AB (trypsin-treated). Among various carbohydrates tested, the lectin was best inhibited by d-galactose and its derivatives with pronounced preference for lactose (3.13mM). N-terminal amino acid sequence of AMML was determined as ESGINLQGDATLANN. The optimal pH range for lectin activity was between pH 4.5 and 7.5, and the lectin was active up to 65°C. It also exerted antifungal activity against Botrytis cincerea, Fusarium oxysporum, Colletorichum sp., and Drechslera turcia but not against Rhizoctonia solani and Mycosphaerella arachidicola. [Copyright &y& Elsevier]

The objective of present study is mainly to compare the discrimination capabilities of near-infrared Fourier transform Raman (FT-Raman, λ ex ~1064 nm) and visible-Raman (vis-Raman, λ ex ~532 nm) spectroscopy for animal fats. All animal fats were analysed by gas chromatography and then the spectra were collected using FT-Raman and vis-Raman spectroscopy. Although vis-Raman spectra partly presented fluorescence interference, the resulting Raman spectra still had higher signal-to-noise ratio than the corresponding FT-Raman spectra. Animal fats from different species produced obvious differences in peak intensity ratios at 1653/1745 and 1653/1441 cm−1, as well as shoulders at 1298 and 1264 cm−1. Chemometric analysis demonstrated that vis-Raman spectroscopy possessed better discrimination capability for animal fat compared to FT-Raman spectroscopy. According to the chemical structure-related basis, the differing degrees of unsaturated fatty acid and relative cis/trans fatty acid contents could be sufficiently reflected in Raman spectra and were the important factors for the successful species discrimination of animal fat. • Animal lipids were analysed by GC and showed different fatty acid constituents. • FT-Raman and vis-Raman spectra of animal fats were compared in detail. • Characteristic Raman peaks and constituents of animal fat were highly correlated. • FT-Raman and vis-Raman spectroscopy were quick and reliable discrimination methods. • Degree of unsaturation and relative cis/trans fatty acids were important factors. [ABSTRACT FROM AUTHOR]