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Estimating nutrient contents of pig slurries rapidly by measurement of physical and chemical properties

Published online by Cambridge University Press:  02 June 2006

Z. YANG
Affiliation:
Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, College of Engineering, China Agricultural University, Beijing 100083, P. R. China
L. HAN
Affiliation:
Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, College of Engineering, China Agricultural University, Beijing 100083, P. R. China
Q. LI
Affiliation:
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P. R. China
X. PIAO
Affiliation:
Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing 100083, P. R. China

Abstract

Livestock slurries contain organic matter, nitrogen, phosphorus and potassium, which can be recycled in arable systems; however, excess applications can cause pollution problems and therefore an estimation of the nutrients in slurries is necessary. The aim of the present experiment was to test methods of rapidly estimating nutrient contents of pig slurries based on physical and chemical analysis. A total of 216 samples with a dry matter content in the range 22·5 to 91·8 g/kg were collected in Beijing during 2003 and 2004. One part of each original sample was analysed for physical and chemical properties, such as electrical conductivity (EC), pH and specific gravity (SG). The other part of each original sample was analysed in the laboratory to measure organic matter (OM), total nitrogen (TN), ammonium nitrogen (AN), total phosphorus (TP) and total potassium (TK). Analytical results showed that OM could be estimated by a single property regression with SG (R2=0·92, P<0·001), TN could be estimated by a multiple property regression with SG and pH (R2=0·86, P<0·001), AN could be estimated by a single property regression with EC (R2=0·82, P<0·001), TP could be estimated by a single property regression with SG (R2=0·81, P<0·001), and TK could be estimated by a multiple property regression with EC and SG (R2=0·81, P<0·001).

Type
Animals
Copyright
© 2006 Cambridge University Press

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