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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]