Rapid Analysis of Hog Manure and Manure‐amended Soils Using Near‐infrared Spectroscopy

Abstract

Application of hog (Sus domesticus) manure to agricultural land converts waste to fertilizer. Nevertheless, matching nutrients in highly variable manure to soil or crop needs requires analytical capability that is ideally field portable and cost‐effective. This study explored using rapid nondestructive near‐infrared spectroscopy (NIRS) to analyze nutrients in hog manure and receiving soil. Spectral data in the visible and near‐infrared (NIR) region (400–2500 nm) from manure samples were correlated with chemical analytical data from the same samples using multiple linear regression statistics to develop calibrations for the prediction of future unknown samples. For 64 manure samples from seven manure storage facilities, r2 between NIR‐predicted values and chemically measured values was 0.93 to 0.99 for NH4–N, total dissolved N (TDN), suspended N, soluble reactive P (SRP), total dissolved P (TDP), suspended P, suspended C, Na, and Mg. For K, Ca, conductivity, and pH, r2 was >0.80. Subsequent analysis of 75 samples from 25 facilities gave similar or slightly less successful results. Soil samples collected before and following application of manure were scanned in a field‐moist state and after drying. For field‐moist soil, r2 for N, organic matter, Mg, and moisture was >0.84; for SO4–S was 0.7. For dry soil, results were similar for N and better for Mg SO4–S, Ca, and K. Near‐infrared spectroscopy has potential to predict some nutrient and salt concentrations in manure rapidly and without sample preparation. It can determine moisture, organic matter, total N, and Mg in field‐moist or dry soil and SO4–S, Ca, and possibly K in dry soil.

Relationships among slurry characteristics and gaseous emissions at different types of commercial Spanish pig farms

“…However, Galassi et al (2010) did not observe a significant influence of supplementation of the diet with a mixture of 20% of wheat bran and 4% of sugar beet pulp on ammonia emissions and Triolo et al (2011) reported a high negative correlation (r=-0.952; p<0.001) between lignin content in the manure VS and its biochemical methane potential. Moreover, Bindelle et al (2009) demonstrated that the substitution of sugar beet pulp with a source of insoluble fibre, as oat hulls, decreased the synthesis of bacterial protein in the gut and the ratio faecal N:urinary N to levels similar or lower than those reached with the standard non sup- ally high which confirms the findings of other studies (Malley et al, 2002;Saeys et al, 2005;Ye et al, 2005;Sørensen et al, 2007) on the usefulness of this methodology to predict main chemical components of fresh slurry and the biochemical CH 4 potential in a range of organic substrates (Doublet et al, 2013;Triolo et al, 2014). The poor prediction for pH could be related to internal correlations to organic compounds giving no true correlations between pH and spectra absorbance peaks (Huang et al, 2007).…”

“…Also, spectroscopic methods, as near infrared reflectance spectroscopy (NIRS) have found increasing use in the laboratory for low cost and rapid analysis, and offer a great potential for on-farm testing (Saeys et al, 2005). They have recently been applied for pig slurry analyses, and useful and accurate NIRS calibrations have been obtained for dry matter, ammonia N, total N and C (Malley et al, 2002;Saeys et al, 2005;Ye et al, 2005;Sørensen et al, 2007). Predicting methane potential emission using NIRS has also been recently a focus of interest to optimize anaerobic co-digestion processes (Doublet et al, 2013;Triolo et al, 2014).…”

The Use of Discriminant Partial Least Squares (DPLS) Method and Spectral Data in Ultraviolet-Visible Region for Classification of Indonesian Palm Civet Coffee

“…Plot panjang gelombang dan koefisien regresi pada model DPLS dengan 7 peubah laten. (Williams dan Sobering, 1996;Malley et al, 2002;Lorenzo et al, 2009). Untuk kalibrasi model kita peroleh nilai RPD yang tinggi (RPD = 3.75).…”

Predicción de propiedades del suelo de importancia agronómica por espectroscopia de reflectancia de infrarrojo cercano

“…In applications of NIR, a calibration model is a statistical correlation between the spectral data of a group of samples with the value obtained by the conventional analysis for a property (Malley et al, 2002;Brunet et al, 2007). Calibration models were generated using the modified method of least squares regression (MPLS) with the WinISI-4 software version 4.2 (Intrasoft International, LLC, 2010).…”

“…En las aplicaciones del NIR, una calibración es un modelo de correlación estadística entre los datos espectrales de un grupo de muestras con el valor obtenido por el analisis convencional para una propiedad (Malley et al, 2002;Brunet et al, 2007). Los modelos de calibracion fueron generados utilizando el método de regresión de mínimos cuadrados modificados (MPLS) por sus siglas en inglés con el software WinISI-4 version 4.2 (Intrasoft International, LLC, 2010).…”