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Poult Sci 2008. 87:1281-1286. doi:10.3382/ps.2007-00464
© 2008 Poultry Science Association
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ENVIRONMENT, WELL-BEING, AND BEHAVIOR

Rapid Analysis of Layer Manure Using Near-Infrared Reflectance Spectroscopy1

L. Xing*,{dagger}, L. J. Chen*,{ddagger} and L. J. Han*,{ddagger},2

* College of Engineering, China Agricultural University, Beijing 100083, China; {dagger} Industrial Technology Service Center, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China; and {ddagger} Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, Beijing 100083, China

2 Corresponding author: hanlj{at}cau.edu.cn


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Diverse samples (n = 91) were used to investigate the feasibility of near-infrared reflectance spectroscopy (NIRS) technology for rapid analysis of the nutrient composition of layer manure. Near-infrared reflectance spectroscopy calibration models for moisture, organic matter, total N, ammonium N, total P, total K, Cu, Fe, Mg, and Na were developed by using the modified partial least squares method. Results showed that the NIRS method could provide accurate predictions of moisture, organic matter, total N, and ammonium N concentrations, with a correlation coefficient of validation (rv2) and a ratio of SD over the root mean square error of prediction (RPD) of 0.86 (2.68), 0.89 (2.91), 0.88 (2.75), and 0.88 (2.62), respectively. Total P (rv2 = 0.80, RPD = 2.01) could be approximately determined by NIRS. It was difficult to determine total K (rv2 = 0.58, RPD = 1.51), Cu (rv2 = 0.48, RPD = 1.38), Fe (rv2 = 0.55, RPD = 1.47), Mg (rv2 = 0.60, RPD = 1.51), and Na (rv2 = 0.62, RPD = 1.87) by NIRS.

Key Words: near-infrared reflectance spectroscopy • modified partial least squares • layer manure • prediction


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Livestock manure contains a wide variety of plant nutrients (e.g., N, P, K) that are recycled in agronomic systems for the production of food and fiber for humans and feeds for livestock (Zhang et al., 2002). However, environmental problems may arise if excess manure is applied to land within sensitive catchments (Nicholson et al., 1996). Two of the main sources of pollution in animal manure are N and P (Williams, 1995). Nitrogen and P in manure contribute to buildup of these elements in the soil and the pollution of water courses via leaching. Ammonia originating from the N is a volatile component that adversely affects air quality. Therefore, reliable information on the nutrient content of animal manures will facilitate their use as organic fertilizers and reduce any associated potential environmental problems.

Traditional analyses in the laboratory have provided great precision, but these have come at the expense of time and resources and have not been attractive to many farmers, or even practical for farming application of animal manure (Lugo-Ospina et al., 2005). Therefore, there has been a continuing interest in rapidly estimating nutrient concentration of livestock manure. These rapid estimation methods can be subdivided into 2 types: physicochemical models and near-infrared reflectance spectroscopy (NIRS) models. The former relate nutrient concentrations to the physicochemical properties (e.g., specific gravity, electrical conductivity, pH). The latter apply NIRS information to describe nutrient concentrations. Compared with physicochemical models, NIRS models offer the possibility of rapid analysis of samples without generating chemical waste. In addition, the NIRS method often requires little or no sample preparation and can determine several constituent concentrations simultaneously. Once NIR calibration models are obtained from the laboratory, they can be applied to field-portable or mobile NIR instrumentation, which has the potential for real-time analysis during the spreading of manure.

The NIRS technique has been applied extensively for the analysis of constituents in livestock manure. Previous work has demonstrated the potential of NIRS for the rapid evaluation of moisture or DM content, organic matter (OM), total N (TN), ammonium N (AN), total P (TP), and total K (TK) in dairy, poultry, swine, and cattle manures (Nakatani and Harada, 1995; Nakatani et al., 1996; Kinoshita et al., 1997; Smith et al., 1999; Millmier et al., 2000; Reeves and Van Kessel, 2000a,b; Reeves, 2001a,b; Malley et al., 2002; Saeys et al., 2004). Recently, our laboratory also presented an NIRS model to predict nutrient contents in fattening pig manure obtained from floor scrapings (Yang et al., 2006). Because of the differences in the diets and physiology of livestock species, the manure produced by each has different chemical and physical properties. For example, dairy manure is a slurry with a high moisture concentration (80 to 95%), but may also contain large amounts of straw or other bedding materials (Reeves, 2001a). Layer manure, on the other hand, is generally much drier but contains minerals such as Mg and Cu. A study by Reeves and Van Kessel (2000a) indicated that the variation in sample composition had a drastic effect on the spectra. Therefore, further work is needed to determine the feasibility and limitations of using the NIRS method to analyze layer manure. The object of this study was to determine the possibility of NIRS to predict the composition different of layer manure samples quickly and accurately, and then to establish NIRS calibrations on layer manure samples.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
Collection and Preparation of Manure Samples
Layer manure samples (n = 91) were collected from 61 farms in the Shunyi, Pinggu, and Yanqing districts of Beijing in the autumn of 2006. Samples were obtained from floor scrapings. Approximately 3 kg of each manure was collected from different points in the henhouses, samples were mixed, and subsamples were taken for various analyses. Sample moisture values ranged from 62.4 to 86.8%. Samples were frozen from the time of arrival at the laboratory. Before NIRS scanning of the layer samples, the samples were thawed overnight at 4°C.

Wet Chemical Analysis
Chemical analysis of the contents included moisture, OM, TN, AN, TP, TK, Mg, Cu, Fe, and Na. Analysis was done by conventional chemical procedures as detailed in Table 1Go.


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  		Table 1. Laboratory analysis methods used
 
Spectroscopic Analysis
All work was performed on a Spectrum One NTS NIRS system (PerkinElmer, Boston, MA). The manure samples were packed in a 10-cm-diameter rotating circular quartz cell. Each of the 91 manure samples was scanned (64 coadded scans) from 1,000 to 2,500 nm at 2-nm intervals.

Statistical Analysis
Mathematical treatment of the spectral data was performed by using Spectrum Quant+ software (PerkinElmer). Near-infrared reflectance spectroscopy calibration was developed by means of modified partial least squares regression. The following mathematical treatments were applied separately and simultaneously, and then compared to choose the best treatment combination: smoothing, derivative, standard normal variate, and normal multiplicative scatter correction. Cross-validation was carried out to select the optimal number of terms in the equation and so to avoid overfitting. The data set was sorted by composition, and 1 of the 4 samples was removed for the validation sample. The mathematical treatment giving the lowest root mean square error of cross-validation on the whole data set for each parameter was applied to the calibration set, and the resultant equation was used to predict the validation set. The following statistical parameters were considered: the correlation coefficient of calibration (r2), the correlation coefficient of cross-validation (rcv2), the correlation coefficient of validation (rv2), the root mean square error of calibration, and the root mean square error of cross-validation. The ratio of SD over root mean square error of prediction, called the ratio of prediction to deviation (RPD), is the factor by which the prediction accuracy has been increased compared with using the mean composition for all samples. Based on the RPD value, 5 levels of prediction accuracy were considered in this study for a very heterogeneous material such as manure. A value for the RPD below 1.5 indicates that the calibration is not usable. A value for the RPD between 1.5 and 2.0 reveals the possibility of distinguishing between high and low values, whereas a value between 2.0 and 2.5 makes approximate quantitative predictions possible. For values between 2.5 and 3.0, and above 3.0, the prediction is classified as good or excellent, respectively (Saeys et al., 2005).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
Chemical Analysis
The minimum, maximum, mean value, and SD of the chemical parameters are summarized in Table 2Go. The data set represented a wide range in composition. The correlation matrix among the chemical parameters is shown in Table 3Go. The concentrations of OM and TP were highly correlated with that of moisture, with r2 values of 0.82 and 0.73, whereas the concentration of AN was highly correlated with TN content, with r2 values of 0.81. Similar correlations were also observed by Saeys et al. (2004, 2005). These researchers suggested that the spectroscopic basis of the former was expected to rely mostly on the O-H and C-H bonds, whereas the latter would rely more on the presence of N-H bonds.


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  		Table 2. Descriptive statistics for chemical constituents in layer manure
 

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  		Table 3. Correlation coefficients (r2) between chemical constituents in layer manure
 
Sample Spectra
The effect on spectra of the variation in sample composition is demonstrated in Figure 1Go. The group of peaks observed provided adequate information for calibration (Figure 1Go). The 2 large absorbance peaks of approximately 7,000 cm–1 (1,429 nm) and 5,000 cm–1 (2,000 nm) were largely attributed to water.


Figure 1
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  		Figure 1. Near-infrared spectra of 91 layer manure samples.

 
Outliers
Outliers were removed on the basis of being labeled as compositional outliers based on the criterion that the predicted versus actual difference for the sample was 3 SD from the mean difference (Yang et al., 2006). On the basis of this method, 1, 3, 3, 3, 2, 6, 5, 3, 4, and 3 samples were removed for moisture, OM, TN, AN, TP, TK, Cu, Fe, Mg, and Na, respectively. Removing these outliers gave somewhat better cross-validation results.

Constituent Partial Least Squares Type 1 Calibration (Three-Fourths of the Samples)
The partial least squares results based on three-fourths of the samples are presented in Table 4Go. Comparison of the rcv2 on the whole data set with the r2 on the calibration set showed that all calibrations were robust. Both the one-out cross-validation results and the final calibration were quite good for moisture, TN, AN, and OM; moderate for TP; and poor for TK, Cu, Fe, Mg, and Na.


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  		Table 4. Partial least squares type 1 analysis results for layer manure (calibration set, three-fourths of the samples)
 
Validation (One-Fourth of the Samples)
The rv2, root mean square error of prediction, RPD, bias, and slope are shown in Table 5Go, with independent validation considered. The comparison between NIRS predictions and the chemically measured values for constituents in manure are shown in Figure 2Go.


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  		Table 5. Validation statistics for the predicted compositions of layer manure (validation set, one-fourth of the samples)
 

Figure 2
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  		Figure 2. Comparison between near-infrared reflectance spectroscopy (NIRS) predictions and the chemically measured values for chemical constituents in layer manure (validation set). OM = organic matter; TN = total N; AN = ammonium N; TP = total P; TK = total K.

 
The rv2 was high for moisture, OM, TN, AN, and TP and low for TK, Cu, Fe, Mg, and Na, which was consistent with the calibration statistics. Root mean square error of prediction values greater than 2.5 and rv 2 values greater than 0.86 for moisture, OM, TN, and AN indicated that good quantitative predictions could be made for these constituents. These results were comparable to those found by other researchers (Millmier et al., 2000; Reeves, 2001a; Reeves and Van Kessel, 2000a,b; Saeys et al., 2005). Saeys et al. (2005) found that OM and TN in pig manure was predicted well by the NIRS method. Reeves and coworkers (Reeves, 2001a; Reeves and Van Kessel, 2000a,b) presented some NIRS models to determine AN and TN concentrations accurately in dairy and poultry manure. These constituents were predicted excellently by NIRS and had a solid spectral basis. The strongest absorbers in the NIR region were O-H, such as moisture, and groups such as C-N, N-H, and C=O characteristic of OM. Therefore, we also expected that the NIRS method could accurately estimate moisture, OM, TN, and AN in layer manure.

For TP, the rv2 value (0.80) and the RPD value (2.01) indicated the possibility of making approximate predictions. Phosphorus in the manure arose from 2 major sources: undigested phytate and excess dietary phosphate (Malley et al., 2002). At present, there are different opinions on the predictability of TP by the NIRS method. The reports of some researchers have indicated that P in poultry (Reeves, 2001a), dairy (Millmier et al., 2000; Reeves and Van Kessel, 2000b), and hog (Millmier et al., 2000) manure cannot be predicted successfully by the NIRS method. However, other researchers have demonstrated that the NIRS method can be applied to predict P concentration in hog (Malley et al., 2002) and broiler (Smith et al., 2001) manure. The reason may be attributed to the presence of spectrally active P bonds. Some studies have suggested that P may exist in forms detectable by NIRS, at least in some grasses and legumes (Clark et al., 1987; Saiga et al., 1989). DeBoever et al. (1994) reported that the P in feed-stuffs could be predicted by NIRS, with an rv2 of 0.94 to 0.96 and a standard error of prediction (SEP) of 0.08.

For TK, Mg, and Na, rv2 values between 0.58 and 0.62 and RPD values between 1.5 and 2.0 indicated that only a distinction between low and high concentrations could be made. For Cu and Fe, rv2 values between 0.48 and 0.55 and RPD values below 1.5 indicated that the calibrations were not usable. These results were consistent with previous studies (Reeves, 2001a; Yang et al., 2006). The poor prediction results for TK, Cu, Fe, Mg, and Na could be explained by the fact that there was a lack of spectral absorption for minerals in the NIR region and that calibrations were generally not good because they depended on the relationships between organic components and the minerals, which are indirect or surrogate calibrations (Shenk et al., 1992). The problem with such surrogate calibrations is that they are only as accurate as the correlation between the organic component and the mineral of interest and can easily produce erroneous results (Yang et al., 2006).

In conclusion, this is one of the first studies to use NIRS to predict the constituents in layer manure. Results demonstrated that NIRS analysis of layer manure samples can provide accurate prediction of a wide range of chemical components, including moisture, OM, TN, and AN. This study presented the opportunity to use the NIRS technique to determine the content of layer manure samples. Further efforts with larger data sets are needed to better determine the feasibility of, limitations of, and requirements for developing accurate and robust calibrations of manure constituents by using the NIRS technique.


   ACKNOWLEDGMENTS
 
The authors gratefully acknowledge the members of Animal Nutrition and Husbandry of the Agricultural Research Center from the Ministry of the Flemish Community (Brussels, Belgium) for their helpfulness and expert suggestions.


   FOOTNOTES
 
1 Supported by the Excellent Young Teachers Program of Ministry of Education, Beijing, P. R. China. Back

Received for publication November 18, 2007. Accepted for publication March 26, 2008.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
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Malley, D. F., L. Yesmin, and R. G. Eilers. 2002. Rapid analysis of hog manure and manure-amended soils using near-infrared spectroscopy. Soil Sci. Soc. Am. J. 66:1677–1686.[Abstract/Free Full Text]

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Reeves, J. B. 2001a. Near-infrared diffuse reflectance spectroscopy for the analysis of poultry manures. J. Agric. Food Chem. 49:2193–2197.[CrossRef][Web of Science][Medline]

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