Hostname: page-component-745bb68f8f-kw2vx Total loading time: 0 Render date: 2025-01-12T13:48:29.037Z Has data issue: false hasContentIssue false
  • English
  • Français

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 ,
L. HAN ,
Q. LI  and
X. PIAO
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
Get access Rights & Permissions [Opens in a new window]

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
Information
The Journal of Agricultural Science , Volume 144 , Issue 3 , June 2006 , pp. 261 - 267
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)