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Refining Soil Organic Matter Determination by Loss-On-Ignition

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Refining Soil Organic Matter Determination by Loss-On-Ignition Pedosphere 21(4): 473–482, 2011 ISSN 1002-0160/CN 32-1315/P c 2011 Soil Science Society of China Published by Elsevier B.V. and Science Press Refining Soil Organic Matter Determination by Loss-on-Ignition∗1 M. H. SALEHI1,∗2, O. HASHEMI BENI1, H. BEIGI HARCHEGANI1, I. ESFANDIARPOUR BORUJENI2 and H. R. MOTAGHIAN1 1Department of Soil Science, College of Agriculture, Shahrekord University, Shahrekord (Iran) 2Department of Soil Science, College of Agriculture, Vali-e-Asr University, Rafsanjan (Iran) (Received December 7, 2010; revised March 22, 2011) ABSTRACT Wet oxidation procedure, i.e., Walkley-Black (WB) method, is a routine, relatively accurate, and popular method for the determination of soil organic matter (SOM) but it is time-consuming, costly and also has a high potential to cause environmental pollution because of disposal of chromium and strong acids used in this analysis. Therefore, loss-on-ignition (LOI) procedure, a simple and cheap method for SOM estimation, which also avoids chromic acid wastes, deserves more attention. The aims of this research were to study the statistical relationships between SOM determined with the LOI (SOMLOI)andWB(SOMWB) methods to compare the spatial variability of SOM in two major plains, Shahrekord and Koohrang plains, of Chaharmahal-va-Bakhtiari Province, Iran. Fifty surface soil samples (0–25 cm) were randomly collected in each plain to determine SOM using the WB method and the LOI procedure at 300, 360, 400, 500 and 550 ◦Cfor2 h. The samples covered wide ranges of soil texture and calcium carbonate equivalent (CCE). The general linear form of the regression equation was calculated to estimate SOMLOI from SOM obtained by the WB method for both overall samples and individual plains. Forty soil samples were also randomly selected to compare the SOM and CCE before and after ignition at each temperature. Overall accuracy of the continuous maps generated for the LOI and WB methods was considered to determine the accordance of two procedures. Results showed a significant positive linear relationship between 2 SOMLOI and SOMWB. Coefficients of determination (R ) of the equations for individual plains were higher than that of the overall equation. Coefficients of determination and line slopes decreased and root mean square error (RMSE) increased with increasing ignition temperature, which may be due to the mineral structural water loss and destruction of carbonates at higher temperatures. A temperature around 360 ◦C was identified as optimum as it burnt most organic carbon, destroyed less inorganic carbon, caused less clay structural water loss, and used less electrical energy. Although the trends of SOM in the kriged maps by the two procedures accorded well, low overall accuracy was observed for the maps obtained by the two methods. While not suitable for determination where high accuracy is required, determination of organic carbon through LOI is likely suitable for exploratory soil surveys where rough estimation of organic matter is required. Key Words: calcium carbonate equivalent, ignition temperature, kriged maps, spatial variability, wet oxidation Citation: Salehi, M. H., Hashemi Beni, O., Beigi Harchegani, H., Esfandiarpour Borujeni, I. and Motaghian, H. R. 2011. Refining soil organic matter determination by loss-on-ignition. Pedosphere. 21(4): 473–482. INTRODUCTION Pitts et al., 1986). It strongly affects physico-chemical and biological properties of soils like cation exchange Soil organic matter (SOM) is defined as the sum- capacity, soil structure, water infiltration rate, water mation of plant and animal residues at various stages holding capacity, soil erodibility and conservation, and of decomposition, cells and tissues of soil organisms, pesticide adsorption (Schulte, 1995; Ding et al., 2002). and well-decomposed substances (Brady and Weil, There are several methods to determine SOM, each 1999). SOM, as one of the most influential of the agri- method with some advantages and disadvantages re- cultural soil parameters, is a useful indicator of soil garding convenience, accuracy, and expense (Nelson fertility and a crucial factor in the soil dynamics of va- and Sommers, 1982). For example, wet oxidation pro- rious agrochemicals (Page, 1974; Krishnan et al., 1981; cedure, the Walkley-Black (WB) method (Walkley and ∗1Supported by Shahrekord University, Iran. ∗2Corresponding author. E-mail: [email protected]. 474 M. H. SALEHIet al. Black, 1934), is a routine, relatively accurate, and termined using both mentioned methods in two major cheap popular method for the determination of SOM, plains of Chaharmahal-va-Bakhtiari Province, Iran. but it involves the use of chromate and generates ha- zardous wastes. Elemental carbon analyzers are accu- MATERIALS AND METHODS rate but are expensive to purchase and maintain. Ano- Study area and soil sampling ther method, loss-on-ignition (LOI), which involves combustion of samples at high temperatures and mea- Fifty surface (0–25 cm) soil samples were randomly suring weight loss after ignition, has been proposed to collected from both the Shahrekord and Koohrang be an inexpensive and convenient method for estima- plains (totally 100 samples) in Chaharmahal-va- tion of SOM (Cambardella et al., 2001; Konen et al., Bakhtiari Province, Iran (Fig. 1). The positions of all 2002) which also avoids chromic acid wastes. samples were determined by GPS for geostatistical The ability of the LOI method to determine SOM analysis. The Shahrekord Plain (50◦ 51 E, 32◦ 19 N, content has been considered reliable (Howard and 2 060 m above the sea level) is located in a semi-arid Howard, 1990; Dean, 1999; Abella and Zimmer, 2006; region with an annual mean precipitation of 320 mm Brunetto et al., 2006; Escosteguy et al., 2007). Howe- and an annual mean temperature of 11.8 ◦Candthe ver, optimal heating temperatures and durations to Koohrang Plain (50◦ 07 E, 32◦ 26 N, 2 285 m above maximize SOM combustion, while minimizing inor- the sea level) is located in a semi-humid region with an ganic carbon combustion, are difficult to determine. annual mean precipitation of 1 440 mm and an annual Both of these variables can substantially affect LOI re- mean temperature of 9.4 ◦C. Both plains are the most sults (Ben-Dor and Banin, 1989; Schulte et al., 1991). important agricultural lands in the province. Higher temperatures can also drive off structural water from clays and other inorganic constituents. Soil sample analyses However, the LOI method has been widely used for All soil samples were analyzed to determine SOM ◦ estimating SOM in a muffle furnace at 360 C for 2 by the WB method (Nelson and Sommers, 1982) and h(Schulteet al., 1991; Konen et al., 2002; Brunetto the LOI procedure (Schulte and Hopkins, 1996). For et al., 2006; Escosteguy et al., 2007; Yerokun et al., the LOI analyses, the soil samples were air dried and ◦ 2007) and at 300, 450, and 600 C for 2 h (Abella and sieved through a 2-mm sieve. The samples were then Zimmer, 2006). oven-dried at 105 ◦C overnight, cooled in a desicca- Accurate estimation of within-field SOM is cur- tor, and weighed before they were combusted at 300, rently an important priority for precision agriculture, 360, 400, 500 and 550 ◦C for 2 h in a muffle fur- given its importance in defining precise fertilizer and nace (Model Exation 1200-30 L). After combustion, pesticide management practices, thus optimizing field the samples were cooled in a desiccator and weighed productivity and minimizing groundwater contamina- again. An estimation of SOM percentage from the loss- tion risks (Sudduth and Hummel, 1996; Ingleby and on-ignition method (SOMLOI) was calculated by the Crowe, 2001). The development of SOM field maps is following equation (Schulte and Hopkins, 1996): also currently an important aspect of precision agri- culture. Although many new techniques are currently SOMLOI = [(soil weight after combustion − oven-dry being developed, geostatistical methods such as kri- soil weight)/oven-dry soil weight] × 100 (1) ging, is most commonly used in mapping SOM levels on a field scale (Chen et al., 2000; Fox and Sabbagh, The soil samples were also analyzed for particle 2002). The application of geostatistics in soil science size distribution following the procedure of Gee and ensures a quantitative description of the spatial vari- Bauder (1986) and for calcium carbonate equivalent ation of soils, improves accuracy in the estimation of (CCE) using the method suggested by Loeppert and soil properties for data interpolation and map compi- Suarez (1996). lation, and forms the basis for a rational design of soil Statistical analyses sampling (Webster, 1985). The objectives of this study were: 1) to establish The general linear form of the regression equation the equations between WB and LOI methods in or- was calculated to estimate SOMLOI after ignition at der to determine the optimum temperature for ignition different temperatures (300, 360, 400, 500 or 550 ◦C) and 2) to compare the spatial variability of SOM de- for 2 h, from SOM obtained by the WB method SOIL ORGANIC MATTER DETERMINATION 475 Fig. 1 Location of the study area in central Iran and the sample locations in the plains studied in Chaharmahal-va-Bakhtiari Province. (SOMWB) for both overall samples and individual for the samples which were partitioned into three ar- plains as follows: bitrary groups based on clay content and CCE values. SOMWB = b0 + b1SOMLOI (2) Geostatistical analysis where b0 and b1 are the intercept and slope in the equa- Geostatistics is based on the theory of a regiona- 2 tion, respectively. Coefficient of determination (R ) lized variable (Matheron, 1971), which is distributed and root mean square error (RMSE) were calculated in space (with spatial coordinates) and shows spatial for Eq. 2. RMSE was calculated using the following auto-correlation such that samples close together in equation: space are more alike than those that are further apart. n Geostatistics uses the technique of variography, i.e., P x − M x 2 calculating variogram or semivariogram, to measure [ ( i) ( i)] i=1 the spatial variability and dependency of a regionali- RMSE = (3) n zed variable.
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