Stepwise chemical digestion, near-infrared spectroscopy or total N measurement to take account of decomposability of plant C and N in a mechanistic model

Henriksen, Trond Maukon; Korsæth, Audun; Breland, Tor Arvid; Stenberg, Bo; Jensen, Lars Stoumann; Bruun, Sander; Guðmundsson, Jón; Pálmason, F.; Pedersen, Anders Branth; Salo, Tapio

doi:10.1016/j.soilbio.2007.06.023

Cited by 12 publications

(17 citation statements)

References 38 publications

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“…In decomposition studies, proximate analyses are commonly used to determine plant material composition (Quemada and Cabrera 1995;Henriksen and Breland 1999;Gunnarsson and Marstorp 2002;Seguin et al 2002;Henriksen et al 2007). A widely used proximate analysis is the neutral detergent solution (NDF)/acid detergent solution (ADF) method (Goering and van Soest 1970) with which plant materials are extracted under reflux successively with a NDF and an ADF.…”

Section: Introductionmentioning

confidence: 99%

Influence of non-cellulose structural carbohydrate composition on plant material decomposition in soil

et al. 2008

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The C mineralisation pattern during the early stage of decomposition of plant materials is largely determined by their content of different carbohydrates. This study investigated whether detailed plant analysis could provide a better prediction of C mineralisation during decomposition than proximate analysis [neutral detergent solution (NDF)/acid detergent solution (ADF)]. The detailed analysis included sugars, fructans, starch, pectin, cellulose, lignin and organic N. To determine whether differences in decomposition rate were related to differences in hemicellulose composition, the analysis particularly emphasised the concentrations of arabinose and xylose in hemicelluloses. Carbon dioxide evolution was monitored hourly in soil amended with ten different plant materials. Principal component and regression analysis showed that C mineralisation during day 1 was closely related to free sugars, fructans and soluble organic N components (R 2 = 0.83). The sum of non-cellulose structural carbohydrates (intermediate NDF/ADF fraction) was not related to C mineralisation between days 1 and 9. In contrast, a model including starch and protein in addition to the non-cellulose structural carbohydrates, with the hemicelluloses replaced by arabinose and xylose, showed a strong relationship with evolved CO 2 (R 2 =0.87). Carbon mineralisation between days 9 and 34 was better explained by xylan, cellulose and lignin (R 2 =0.72) than by lignocellulose in the ADF fraction. Our results indicated that proximate analyses were not sufficient to explain differences in decomposition. To predict C mineralisation from the range of plant materials studied, we propose a minimum set of analyses comprising total N, free sugars, starch, arabinose, xylan, cellulose and lignin.

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Section: Introductionmentioning

confidence: 99%

Influence of non-cellulose structural carbohydrate composition on plant material decomposition in soil

et al. 2008

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“…Stepwise chemical digestion (SCD) separates organic material into fractions of soluble, cellulose, hemicellulose and lignin-like substances (Mertens et al 2002;Van Soest et al 1991). Using SCD data to partition C and N into the litter pools of mechanistic SOM models can improve predictions of C and N mineralization relative to a default constant value (Probert et al 2005;Quemada and Cabrera 1995), especially if the C/N ratio of each fraction is determined (Henriksen and Breland 1999a;Henriksen et al 2007). However, it is not obvious how such data should be analyzed in order to partition residue C and N into rapidly and slowly decomposing litter pools.…”

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confidence: 99%

“…However, it is not obvious how such data should be analyzed in order to partition residue C and N into rapidly and slowly decomposing litter pools. Water-soluble C and N have been found to underestimate the rapidly decomposing litter pool in the DAISY model (De Neergaard et al 2002), and the use of SCD data in a mechanistic three-litter-pool model resulted in somewhat overestimated plant residue C and N mineralization (Henriksen et al 2007). Henriksen et al (2007) also predicted SCD data from near infrared reflectance spectra (NIR) data, which resulted in similar model performance as using SCD data directly.…”

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confidence: 99%

Estimation of plant litter pools and decomposition-related parameters in a mechanistic model

et al. 2010

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Modeling the C and N dynamics during decomposition of plant residues depends on robust estimation of the litter pool partitioning (LPP) of residues, i.e. the fraction of their C and N allocated to rapidly and slowly decomposing pools. We searched for ways to estimate LPP by analyzing data on C and N mineralization during decomposition of 60 widely different plant residues, using a simple model with two litter pools and one microbial pool. LPP and relevant global parameters were estimated by model optimization (Levenberg-Marquardt-least-squares algorithm) in one operation. These kinetically-defined LPP values were used in regression analyses against data from stepwise chemical digestion (SCD) and near-infrared reflectance (NIR) analysis of the plant residues. LPP predicted by these regression models resulted in better performance than LPP from measured neutraldetergent-soluble (NDS) C and N when validated on independent data (n=15 plant residues). The results demonstrated the potential improvement by simultaneous estimation of residue specific LPP and global parameters, and that kinetically-defined LPP can be equally well predicted by NIR as by total N and NDS-C. Model failure for a minority of the plant-residues could partly be removed by altering the microbial C/N ratio (global optimum 7.1) within the range 5-15, possibly reflecting a variable dominance of bacteria or fungi.

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“…While models differ in the pool structure used to describe the decomposition of organic inputs, with the pools differing in their rates of decomposition (Hadas et al, 2004 andHenriksen et al, 2007), the widely used assumption that all pools have the same C/N ratio, Indian FYM which is not only complex in composition but also poor in quality because of low nutrient value. So, attempt was made to parameterise the Indian FYM for the fisrt time based on its composition to modify SoilN module to successfully predict N mineralised from this material and subsequently used this material as part of the integrated nutrient management system to predict yield of soybean and wheat in a Vertisol of central India.…”

Section: Future Research Needmentioning

confidence: 99%

“…Numerous experiments have shown that N-rich plant materials, such as legume residues (green manures), decompose much faster than N-poor plant materials such as grain straw (Henriksen and Breland, 1999;Korsaeth et al, 2001). While models differ in the pool structure used to describe the decomposition of organic inputs, with the pools differing in their rates of decomposition (Hadas et al, 2004 andHenriksen et al, 2007), the widely used assumption that all pools have the same C/N ratio, results in the failure of the models to adequately represent the observed release of N from organic amendments. Better understanding of the influence of organic matter composition on the rate and extent of N mineralisation, and subsequently parameterising the model based on composition is necessary if this important "commodity"…”

Section: Introductionmentioning

confidence: 99%

Simulation of nitrogen release from organic materials in the soybean/wheat cropping systems on Vertisols in central India

Mohanty¹

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The soybean (Glycine max L.)-wheat (Triticum aestivum, L.) cropping system is the dominant cropping system on Vertisols of Madhya Pradesh in central India, but the yield of soybean has been decreased significantly over recent years due to biotic and abiotic factors. The yield of wheat following soybean in this region is also governed by the availability of irrigation water. So managing this cropping system in terms of nutrient and water in Vertisols is important for soybean and wheat production. The proposed research work was undertaken to analyze N release from different organic materials like rice (Oryza sativa, L.) and wheat straw, green manure (like Gliricidia) and farmyard manure (FYM), modeling N release using APSIM (Agricultural Production System Simulator). The APSIM model has been calibrated for first time for Indian FYM, and also tested for the soybeanwheat cropping system permitting prediction of N release from organic amendments varying in C/N ratios and biochemical properties.A laboratory incubation experiment was carried out for 98 days at 30º C under aerobic conditions to study the effects of rice and wheat straw applied at 5 and 10 g kg -1 to a Vertisol soil in the presence or absence of additional N (as urea). The study showed an interactive effect between the rate of application of the residues and additional mineral N.Without additional N, the mineral N in soil was completely immobilised within two weeks, irrespective of the rate of application; initial net immobilisation was limited by the availability of N and was independent of residue rate. When additional N was supplied, initial net immobilisation was dependent on the rate of application of the residue.We used the APSIM SoilN module to simulate N mineralisation from high C/N ratio crop residues, and compared the predictions with the observed data from our incubation study and other published data sets. Model performance was generally satisfactory with a modelling efficiency of 0.82. The major discrepancy between the modelled and the observed data was a tendency for the model to underestimate the initial rate of immobilisation. iii the model based on the FYM composition. Water soluble C and N components were used to specify the conceptual pool FPOOL1 and the measured lignin content to specify the C content of FPOOL3. Using these values and an iterative approach to identifying an Application of

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Stepwise chemical digestion, near-infrared spectroscopy or total N measurement to take account of decomposability of plant C and N in a mechanistic model

Cited by 12 publications

References 38 publications

Influence of non-cellulose structural carbohydrate composition on plant material decomposition in soil

Influence of non-cellulose structural carbohydrate composition on plant material decomposition in soil

Estimation of plant litter pools and decomposition-related parameters in a mechanistic model

Simulation of nitrogen release from organic materials in the soybean/wheat cropping systems on Vertisols in central India

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