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Comparing the spatial predictions of soil organic matter determined by two laboratory methods
Author(s) -
Frogbrook Z.L.,
Oliver M.A.
Publication year - 2001
Publication title -
soil use and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 81
eISSN - 1475-2743
pISSN - 0266-0032
DOI - 10.1111/j.1475-2743.2001.tb00033.x
Subject(s) - loss on ignition , organic matter , loam , soil science , soil organic matter , environmental science , sampling (signal processing) , linear regression , soil test , soil water , mathematics , statistics , environmental chemistry , chemistry , computer science , organic chemistry , filter (signal processing) , computer vision
. The most common way of assessing soil organic matter content is by loss on ignition, which is both simple and inexpensive. This method tends to overestimate organic matter content because additional weight losses occur during ignition. An alternative, more expensive and time‐consuming method for determining soil organic matter content is by an acid dichromate oxidation. This paper compares the results of applying these methods to soil on different parent materials in two arable fields. Summary statistics and correlation coefficients showed that there were consistent relationships between the two sets of values: the stronger was for the sandy soil and the weaker was for the clay loam. This relationship can be used to improve the accuracy with which soil organic matter content is known while using fewer of the expensive measurements and more of the inexpensive ones. Two approaches to prediction were compared: the geostatistical method of cokriging, and simple linear regression. These were used to predict organic matter determined by an acid dichromate oxidation from the loss on ignition. The estimates from cokriging were more accurate but the method requires the spatial correlation to be modelled reliably. The regression results showed it to be a valuable and practical approach. Using the information from nine carefully selected sampling sites a regression line could be fitted that was representative of the full data.