Composting of animal manure is an effective method to manage organic wastes in agriculture and to reduce environmental and human health risks. Rapid determination of composition of animal manure compost could facilitate the composting process and improve the quality of compost products. This study aimed to explore the feasibility of analysing the composition of animal manure compost based on dried and fresh weight basis by near infrared (NIR) reflectance spectroscopy. A total of 120 animal manure compost samples collected from 22 provinces in China were scanned (4000–10,000 cm^–1) and NIR calibrations were developed by partial least-square regression. Results showed that for dried and milled animal manure compost samples, the NIR method could accurately predict the contents of volatile solid (VS), total organic carbon (TOC) and total nitrogen (TN). Validation coefficient of determination (r²) and the ratio of the standard deviation of the reference data in the validation set to the standard error of prediction (RPD) were 0.85 (2.60), 0.85 (2.56) and 0.97 (6.95), respectively. For fresh animal manure compost samples, the NIR method could provide accurate predictions of moisture, electrical conductivity (EC), volatile solid (VS), total organic carbon (TOC) and total nitrogen (TN) content with r² and RPD of 0.98 (7.48), 0.90 (3.10), 0.94 (4.05) 0.91 (3.17), and 0.97 (6.11), respectively. Poorer calibrations were obtained for pH, C:N ratio and total phosphorus (TP) using either dry or fresh samples. Overall, the results indicated that NIR analysis could be very useful for the rapid quality evaluation of animal manure compost.

Keywords: near infrared (NIR) reflectance spectroscopy, partial least-square (PLS) regression, animal manure compost, dried, fresh