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Journal of Soils and Sediments

, Volume 18, Issue 8, pp 2801–2812 | Cite as

Qualitative and quantitative soil organic matter estimation for sustainable soil management

  • Jolanta Kwiatkowska-Malina
Open Access
Humic Substances in the Environment

Abstract

Purpose

The aims of this paper were to review tools and methods for qualitative and quantitative evaluation of soil organic matter (SOM) coming from diverse egzogenic sources for effective soil management, and to introduce a new approach to predict dynamics of SOM transformations, especially humification, as a key process in the formation of humic substances (HSs).

Materials and methods

A review of existing literature is presented on tools and methods for qualitative and quantitative assessment of organic matter in soil originating from various sources for reasonable soil management, attempting to provide a better understanding of the advances in organic matter transformations and new research directions for modeling. Diverse tools and methods for qualitative and quantitative evaluation of organic matter in soil coming from diverse sources have been adopted so far to express transformation processes.

Results and discussion

For the qualitative analysis of SOM and humic acids (HAs), the analytical techniques are applied, e.g., HPSEC, NMR, and ESI-FTICRMS. The quantitative analysis is done through the following parameters: humification index (HI), humification degree (HD), and humification rate (HR). These analyses indicated that because of lack of reliable data from sufficiently long-term experiments, mathematical modeling may be applied as a numerical tool for quantitative estimation and prediction of humification of SOM. The effective soil management should include soil properties as well as different functions: food production, nutrient and water cycling, storage, filtrating, buffering, biological habitat, gene pool, source of raw materials, climate regulations, heritage, platform for man-made structure. The soil utility value should be evaluated through the SOM qualitative and quantitative analysis of organic carbon and total nitrogen. Knowledge about dynamics of SOM transformations is essential, particularly in the context of stability and efficiency of different sources of organic matter applied into soil. A qualitative understanding of SOM dynamics transformations along with modeling for quantitative assessment of HS formation should be used to develop sustainable soil management. The modeling may be considered as a tool for predicting SOM humification dynamics and consequently the formation of HSs from the diverse sources. The existing archival data from a long-term experiment may be used to build and calibrate the reliable mathematical model of SOM humification.

Conclusions

Managing of SOM remains a sound basis for maintaining soil in a good condition for optimizing productivity. The development of land management strategies to optimize both the increase of soil organic carbon levels and the recycling of nutrients from SOM needs to be a priority. This should include policy makers and other users as well.

Keywords

Humification Mineralization Soil organic matter Sustainable management 

1 Introduction: importance of soil organic matter in sustainable soil management

Soil is largely a non-renewable resource and it provides the basis of ecosystems by performing many key environmental, social, and economic functions: food production (99%), nutrient and water cycling, storage, filtrating and buffering, biological habitat and gene pool, source of raw materials, climate regulations, physical and cultural heritage, platform for man-made structure (Blum 2005; Dominati et al. 2010; Banwart et al. 2017). Soil is extremely important as a filter removing pollution from water and helping to regulate the flow of water through the landscape (Rawls et al. 2003; Keesstra et al. 2012).

Soil organic matter (SOM) is responsible for the provision of these functions of soil nowadays and in the future (Craswell and Lefroy 2001; Leszczynska and Kwiatkowska-Malina 2011; Schmidt et al. 2011). It is a building block for the soil structure, acts as a large carbon sink in the biosphere, and plays an important role in the CO2 balance. It is important both as a driver of climatic change and a response variable to climate change, capable of acting as a source and sink of carbon (Lal 2004a; Smith 2012). Soils also help to regulate other greenhouse gases such as nitrous oxide and methane (Muñoz et al. 2010; Oertel et al. 2016).

A decrease of SOM contents is among eight main threats for soils as indicated in the EU Soil Thematic Strategy (European Environment Agency 2010). Since the mid-nineteenth century, about 108 to 188 Pg C have been lost mostly from terrestrial biomass whereas about 25% of this loss is contributed to SOM mineralization (Zech et al. 1997; Lal 2004a; Houghton 2012). The losses of carbon from soil can be mitigated by recarbonization using management practices thereby increasing food security (Sauerbeck 2001; Lorenz and Lal 2012). Nevertheless, some mitigation options in agriculture are in direct competition with each other, e.g., use of crop residue for second-generation bioenergy crops versus residue inclusion into soil for maintenance or build-up of SOM (Shahbaz et al. 2017). The direction of changes and conversion of SOM nowadays is influenced by land management, especially for agricultural land. Soils easily and quickly lose organic carbon accumulated mainly as SOM (95%—pastures, meadows and 100%—arable land) when natural soils are converted into agricultural soils (Barancikova et al. 2016). It is estimated that soil cultivation, mainly conversion of pasture into arable land, leads to significant organic carbon losses (up to 50 Pg) in the overall balance (Janzen 2006). Information about broad ranges of SOM content is needed for general knowledge of changes in soil quality resulting from management practices that either favor among others soil erosion or accelerated SOM decomposition (Shibu et al. 2006).

SOM can be divided into three main pools: labile, stable, and inert (humins). Generally, it consists of two main fractions: humic substances—HSs (fulvic acids—FAs, humic acids—HAs, humins) and labile organic matter (Gonet et al. 2002; Strosser 2010). Humic substances (HSs) have been found to be a stable material, specific to each type of soil and not markedly changing over decades of soil use (Stevenson 1994).

Man has recognized the importance of organic amendments for maintaining or increasing the content of SOM since he started farming (Allison 1973; Reeves 1997; Scotti et al. 2015). It is, therefore, important to assess safe agricultural use of the exogenous organic matter as the fertilizer or conditioner of soil properties including an increase of SOM content. Organic substances more rich in carbon but poor in nitrogen (e.g., wood, tree coniferous, straw, brown coal) are relatively stable sources of energy and substrates for microorganisms, and can therefore be mineralized and humified relatively slowly (Zech et al. 1997; Kalbitz et al. 2000; Manzoni et al. 2010). This is consistent with the paradigm that improving production and environmental functions of the soil will lead to the increased quality of the environment.

With the above in mind, the paper presents a review of tools and methods for qualitative and quantitative evaluation of SOM coming from diverse sources for effective soil management that are currently important in international research. However, there are only few studies that address and assess the advances in modeling of organic matter transformations in soil.

2 Stability of organic matter in soils

Due to many soil parameters determining soil functions, they are difficult to measure directly, especially over large areas. For these reasons, they are usually assessed by deriving and/or measuring indicators or proxies that correlate with soil conditions, and can be used to assess soil quality and its functioning. Some soil quality indicators are descriptive and can be used in the field while others are quantitative and must be assessed by laboratory analyses. There are three main categories of the soil indicators: chemical (e.g., nutritional status), physical (e.g., hydrological characteristics such as water retention by soil structure), and biological (e.g., soil respiration). Organic matter transcends all three categories of indicators as it is related to all soil functions and is itself an indicator of soil quality. Organic matter within the soil serves several functions. From a practical agricultural standpoint, it is important for two main reasons: (i) as an “evolving nutrient fund” and (ii) as an agent to improve soil structure, maintain tilt, and minimize erosion. SOM has been widely promoted as a key indicator of soil quality, particularly in agricultural soils, but there has been no consensus on what the critical level of it should be in an agricultural soil, and how this level will vary between soils of different textural classes under different environmental conditions. There are several different types of SOM as a result of the way of its formation. In general, three distinct types of SOM have been defined (Stevenson 1994; Six and Jastrow 2002): (i) decomposing plant residues, fungal hyphae, fine plant roots, and associated biomass which usually is unstable and has a short turnover time (< 1–2 years), (ii) an active component that includes partially stabilized organic materials and microbial metabolites—usually referred to as the humus fraction (turn over time of 5–25 years), (iii) resistant fraction which is strongly stabilized either chemically or physically (turnover time of 250–2500 years). SOM while a relatively simple property to measure may be characterized in many different ways (Nortcliff 2002; Jones et al. 2005). The different methods of measuring SOM and soil carbon are based on detecting different organic materials (Conyers et al. 2011).

An organic material, largely plant material, in the initial stages of transformation, is composed of complex carbohydrates (celluloses, polysaccharides), plant proteins (high nitrogen content), cuticular waxes, lipids, and lignin. The resistance of the components within initial transformation of organic materials (from least to most stable) is as follows: simple sugars < amino acids < proteins < cellulose < hemicellulose < fats, starches, and waxes < lignin and tannins. These compounds undergo decomposition releasing CO2 (Fig. 1).
Fig. 1

Flow chart for transfer of C involved in the mineralization and immobilization processes

Understanding of qualitative and quantitative changes of SOM is crucial for agronomic aspects like nutrient dynamics, structure, and water storage capacity, as well as for environmental issues such as biodiversity, nitrate leaching, and carbon storage (Schnitzer et al. 2006). A small part in spite of many works about quality of soil quantitative, estimate only directly, according to Nortcliff (2002), it reflects the difficulty of finding such conducting of reliable index (parameters).

The classification of soil HSs proposed by Stevenson (1994) is based entirely on operational procedures which say that the humin fraction may consist of one or more compound classes including paraffinic substances. Nowadays, thanks to separation and fractionation techniques the isolation and identification of components plenteous in carbohydrates and amino acids and a humin fraction rich in aliphatic carbon is possible. Burdon (2001) referred to the spectroscopic data which show, e.g., that lignin is a major contributor to the aromaticity of HSs. According to Sutton and Sposito (2005), HSs are collections of diverse, relatively low molecular mass components forming dynamic associations stabilized by hydrophobic interactions and hydrogen bonds. Nebbioso et al. (2016) used the humeomics approach to obtain humeome fractions analyzed by high-performance size exclusion chromatography (HPSEC), which were classified according to molecular weight and degree of unsaturation as well as oxygen and nitrogen contents in formulae (Hayes and Swift 2017).

The contribution of lignin to soil humic components is still discussed. Although based on electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICRMS) data, it was shown that carboxylic acids bound to both aliphatic and condensed aromatic molecules are common components of HAs (DiDonato and Hatcher 2016; DiDonato et al. 2016). In their opinion, the vicinities of a variety of carboxylic groups to an assortment of structural components would indicate that “lignin-like” compounds resent the third major molecular group common to HAs (DiDonato et al. 2016).

Using solid-state two-dimensional 1H-13C correlation NMR spectrometry, Cao et al. (2016) proposed primary and secondary structures for a lignite HA fraction. According to Leenheer (2016), HSs are polyphenolic polycarboxylic acids derived from lignin and tannins, and these would form electron-rich (nucleophilic) cavities between stacked aromatic rings at low pH values. This novel approach could provide an interpretation of how naturally occurring and anthropogenic organic chemicals might be bound in SOM.

To evaluate quantitatively SOM and HSs, the following parameters could be applied among others (Sequi et al. 1986; Cavani et al. 2003): humification index (HI), humification degree (HD), and humification rate (HR).
$$ \mathrm{HI}=\mathrm{NH}/\left(\mathrm{HAs}+\mathrm{FAs}\right) $$
$$ \mathrm{HD}=100\times \left(\mathrm{HAs}+\mathrm{FAs}\right)\mathrm{TEC} $$
$$ \mathrm{HR}=100\times \left(\mathrm{HAs}+\mathrm{HAs}\right)/\mathrm{TOC} $$

where NH—contents of fraction, which was not humified (g), HAs—humic acids (g), FAs—fulvic acids (g), TEC—total extracted carbon (g), and TOC—total organic carbon (g).

Data for this evaluation could be obtained from long-term field experiments.

3 Numerical tools useful for management of soil organic matter

Prior to describing the dynamics of SOM, the terminology should be clarified such as carbon turnover, transformation, net decomposition, humification, and mineralization (Shibu et al. 2006). Carbon turnover in general includes mineralization and transfer from one pool to another, containing microbial biomass. Transformation is the process in which a substrate is transformed into organic compounds and CO2, whereas net decomposition accounts for the CO2 component only (Van Keulen 2001). During humification, carbon from organic residues is converted to HSs through biochemical and abiotic processes, whereas mineralization leads to the conversion of organic carbon to CO2 and is, therefore, similar to net decomposition.

Rate equations for transformation of various SOM pools are described by zero-order, first-order, or Michaelis-Menten (Monod) kinetics (Shibu et al. 2006). The process described by zero-order kinetic proceeds at a constant rate, independent of the substrate (C) concentration:
$$ \mathrm{dC}/\mathrm{dt}=\hbox{-} {k}_0, $$
while the rate of a first-order reaction is proportional to the substrate concentration: dC/dt =  ‐ k1C.
In Monod-type kinetics, the reactions are described as biological processes with rates depending on the amount of microbial biomass which is involved in the utilization of a substrate:
$$ \mathrm{dC}/\mathrm{dt}=\hbox{-} \mathrm{dC}/{\mathrm{dt}}_{\left(\max \right)}\mathrm{C}/\left({K}_{\mathrm{c}}+\mathrm{C}\right) $$
where k0 is the rate constant for a zero-order reaction, k1 is the relative rate constant for a first-order reaction, and Kc resents the half-saturation concentration. The rate is equal to half of the maximum ((dC/dt)(max)), defined as a function of microbial biomass.

The studies of genesis of HSs initiated by Williams were concluded by Kononova (1966) that these were (i) humus substances exist in soil as a natural body, (ii) various plant materials which undergo complex biochemical transformations serve as sources of humus substances, (iii) plant materials decompose to more simple products from which the complex HSs are synthesized, and (iv) and microbial enzymatic activity is involved in both the decomposition and the synthesis processes (Hayes and Swift 2017).

At any given time, HSs consist of a range of materials: from the intact original tissues of plants and animals to the substantially decomposed mixtures (Yang and Janssen 2000). SOM is in different stages of biochemical and microbiological transformations, as a result of enzymatic activity of microorganisms (among others fungi and bacteria) as a result of mineralization and humification processes (Chen and Inbar 1993). Some scientists consider the synthesis of HSs to be entirely biological, but there is a growing concept suggesting that the compounds released in the microbial breakdown of organic substrates could condense to give rise to humic products from chemical synthesis processes. However, such structures as humic-type substances are not formed in soil under biological conditions; they could arise from char coming from burning vegetation or amendments containing biochar (Hayes and Swift 1978; Clapp et al. 2005).

In recent years, the view is that organic materials in soils are transformed (humified) into (i) amorphous polymeric, HAs, FAs, and humins and (ii) compounds belonging to recognizable classes, such as carbohydrates, peptides, altered lignins, fats, waxes, cutins, and cutans. These can be synthesized by microorganisms or can arise from alterations of similar materials in the original debris (Hayes and Swift 2017). Nevertheless, according to Lehmann and Kleber (2015), SOM is a “continuum of progressively decomposing organic compounds”, so they would not agree with the concepts of HAs, FAs, and humins.

At present, it can be stated that humification comprises progressive transformations of organic debris, and at some stage during the transformation processes, products are formed that satisfy the definitions for HAs and FAs. These fractions can be further stabilized and retained through associations with the soil clays and the divalent and polyvalent cations, as well as with the oxide minerals, in particular those of aluminum, iron, and manganese (Hayes and Swift 2017). It is unlikely that it will ever be possible to determine a definitive, detailed structure of a single, specific, isolatable “humic compound” or molecule.

Humification is a continuous process, but, given sufficient time, a stage is reached when the degree of transformation is such that there is little or no evidence of decomposed materials (Wadman and de Hann, 1997; Rumpel et al. 1999). According to Hayes and Swift (2017), the materials isolated from composts, sewage sludge or from putrefying materials, should not be regarded as such until these have undergone considerable transformations in soil.

From the mid-1970s, improved computing facilities and better understanding of the processes led to rapid development of dynamic SOM models (Frissel and Van Veen 1981). There are analytical, comprehensive, and summary models (Shibu et al. 2006). Analytical models mostly consider SOM as a single homogenous pool that decomposes with relative rates varying in time. Henin and Dupuis (1945) were among the first to quantitatively describe SOM dynamics. They stated that fresh organic material added to the soil would decline. Kortleven (1963) in addition to Henin and Dupuis (1945) concept assumed that conversion of fresh organic matter into humus can be represented by two constants: the fraction of organic material remaining in the soil after 1 year (i.e., the humus fraction or humification coefficient) and the relative decomposition rate of the humus. Both appeared dependent on the type of organic material used. Kolenbrander (1969) modified the Henin and Dupuis (1945) model, considered as a single pool of total organic matter however, with a relative decomposition rate decreasing with time. On the basis of long-term decomposition experiments with the 14C-labeled plant material in soils of the temperate and tropical origin, Sauerbeck and Gonzalez (1977) concluded that during the first year, 65% of the added material disappeared, and stable residues and turnover products followed an exponentially decreasing function. These results suggest the existence of two independent organic fractions: (i) an easily decomposable labile fraction and (ii) a more resistant stable fraction. Combining labile and stable organic matter fractions, Janssen (1984) proposed to introduce the relative rate of decomposition which was defined as an empirical function of the “initial age” of the organic material. For theoretical analysis of natural ecosystems and simulation of SOM mineralization, humification, and nitrogen release, Chertov and Komarov (1996, 1997) developed the soil organic mineralization model (SOMM). The model consists of variable coefficients: the litter input rate, the non-decomposed part of the litter remaining in the soil, the complex humic substance with non-decomposed plant debris, and the humus content. For long-term predictions of carbon dynamics in soil to estimate carbon sequestration capacity, the introductory carbon balance model (ICBM) has been defined (Andrén and Kätterer 1997). The effect of climatic factors was condensed into a single parameter that similarly affects the decomposition rates of “young” and “old” organic matter. This parameter needs to be optimized for a particular ecosystem based on experimental or available literature data. The value of humification rate is defined depending on the litter quality and external factors such as soil type.

Parnas (1975) applied a process-based modeling approach to simulate SOM dynamics. The models consider SOM as heterogeneous mixtures, and decomposition of components in this mixture, represented by a number of arbitrary pools, takes place at different relative rates. These models were divided into comprehensive and summary models. Generally, the comprehensive model has been designed for research purposes whose essential elements are thoroughly understood (Penning de Vries 1982). The production of annual pasture limited by the rainfall and nitrogen model (PAPRAN) distinguishes two fractions of SOM (Seligman and Van Keulen 1981): fresh (simple sugars, cellulose/hemicellulose, lignin) and stable. The decomposition rate of an organic material is based on the first-order kinetics and depends on soil temperature, soil moisture, and C/N ratio of the substrate. The relative rate of decomposition declines sequentially with increasing C/N ratio as more easily decomposable fractions vanish. Juma and Paul (1981) tested the concepts of soil microbial growth and decay to study mineralization and immobilization of soil N assuming that SOM is resented by active, stabilized, and old pools. The active pool was further sub-divided into biomass, active, and metabolites, while the passive pool into a stabilized component and an old pool with half-lives of around 35 and 600 years, respectively. During decomposition of carbon substrates, a portion of the carbon is transferred to the microbial biomass pool and the remaining part is released as CO2. Microbial biomass undergoes decomposition and decay, 50% of the products goes to the active pool and the rest to the metabolite pool. Both pools also undergo decomposition and are transformed into biomass, while CO2 is released. Part of the active carbon transferred to the stable pool is subsequently transferred to the old pool. The multi-layer, multi-component model, with varying numbers of soil layers and six organic carbon pools, was proposed by Van Veen and Frissel (1981). The decomposition rate of SOM is controlled by the uptake of carbon by microbial biomass. Nitrogen and carbon transformation in soil (NCSOIL) is a module of a model describing C and N flows in the soil-water-air-plant system (NCSWAP) (Molina et al. 1983). In this model, SOM was divided into fresh plant residues, microbial biomass, and labile and stable organic pools. Decomposition of the pools proceeds independently with relative rates that fall below the potential when not enough nitrogen is available for the growing of microbial biomass. The ECOSYS (Ecosystem) model developed by Grant (2001) for natural and managed ecosystems simulates processes of soil water, heat, carbon, oxygen, nitrogen, phosphorus transport, and gaseous (CO2, methane, N2O) exchanges with a range of management options (tillage and residue management). Each organic state in each substrate-microbe complex was further divided into carbohydrate, cellulose, and lignin with varying rates of relative decomposition. Residue decomposition products undergo humification. A fraction of lignin coupled with proteins and carbohydrates is transferred to the solid substrate of active SOM. Products of microbial decomposition are divided between microbial residues, and the solid substrate of the passive SOM substrate-microbe complexes depends on the soil clay content.

Summary models are more suitable for applicative and predictive purposes where essential aspects of comprehensive models are formulated in less detail (Van Ittersum et al. 2002). RothC (Rothamsted carbon) is a transformation model that simulates the turnover of organic carbon in non-waterlogged soils in monthly time intervals and allows for the effects of soil type, temperature, moisture content, and plant cover (Coleman and Jenkinson 1996). In this model, SOM was divided into five components with different decomposition rates. Many concepts in the RothC model are similar to Van Veen and Paul’s (1981) and CENTURY models (Patron et al., 1992). The latter is the SOM model to which a crop growth module has been added. The model simulates the dynamics of carbon, nitrogen, phosphorus, and sulfur for different plant production systems such as grassland, arable land, forests, and savannas. Metabolic and cellulose carbon is transferred to the active pool while lignin C goes directly to the slow pool. The actual relative rate of the structural pool of decomposition is derived from its lignin content, whereas for the active pool, it is modified for soil texture, soil temperature, and the moisture content. This model needs very precise input data acquisition of which is a problem for all agricultural soils in a specific region or country (Barančíková et al. 2016). Field data at diverse temporal and spatial (local/regional) scales are important to obtain the reliable model (Ouyang et al. 2017). The Verberne model (Verberne et al. 1990) describes carbon and nitrogen cycling in different soil types, with the objective of predicting the daily net rate of mineralization. The model divides organic material into three pools of residues and four pools of SOM, based on biochemical composition, the C/N ratio, and the lignin content. The carbon and nitrogen dynamics (CANDY) model allows for specification of up to six categories of fresh organic matter, including manure and/or slurry (Franko et al. 1995). It distinguishes three sets of pools: fresh organic matter (FOM), biologically active organic matter (BOM), and slow cycling SOM. Carbon moves out of each pool with rate constants adjusted for the effect of temperature, soil water content, and soil aeration. The remainder being released as CO2. The SOCRATES model estimates changes in soil organic carbon (SOC) as influenced by the cropping pattern, including pastures (Grace and Ladd 1995). The main advantage in its development was that the input data could be easily measured in the laboratory. At the beginning, 2% of the measured SOC is assumed to be protected and the remaining 98% is defined as stable humus. Carbon was transferred to the microbial biomass, humus, and CO2—the relative decomposition rates for each of the components were calculated using 14C data.

Most modern SOM dynamics models have focused on the mineralization processes (mainly microbial heterotrophic respiration) and have not included the effects of soil fauna that are known as key agents of SOM formation. Komarov et al. (2016) developed a modeling approach predicated on the existence of definable stoichiometric relations among the processes leading to SOM formation that are mediated by soil biota. They developed a food web-based module, using a synthesis of decades of published data, to describe the micro- and meso-faunal excrement and necro-mass production. As well as they developed a separate module for anecic earthworms (i.e., those that have vertical borrows and produce surficial earthworm casts), with explicit representation of processes related to fresh casts. These two modules were compiled and integrated in the ROMUL model of SOM dynamics without changing the structure of the original model. These modules enabled calculation of the proportional contribution of faunal by-products to humification and carbon sequestration. Testing the new version known as the Romul Hum model showed consistent accumulation of faunal by-products in the “final” SOM fractions (Komarov et al., 2016). The main improvement of the Romul Hum model is that the by-products of soil micro- and meso-fauna, and their role as precursors to formation of stable SOM, are included. The ratios of “excrement mass/necro-mass C to soil heterotrophic respiration C” were key parameters introduced into the Romul Hum model. This novel approach to including soil faunal activity in modeling of soil C dynamics takes advantage of the high degree of organization within soil biotic communities in food webs, and it integrates the effects of microorganisms and soil fauna that govern the processes of organic residue transformation and mineralization in the soil system (Chertov et al. 2017a). Quantitative description of the role of soil fauna in SOM formation and dynamics is necessary for further development of modeling. The eco-physiological parameters of food consumption, excretion efficiency, assimilation efficiency, lifespan, and mortality for anecic earthworms were developed using an approach similar to that for food web meso-fauna in the Romul Hum model. The addition of the earthworm module to the Romul Hum model allows for assessment of the combined effects of earthworm cast production and micro- and meso-faunal food web activity within the casts, on the formation of stable SOM (Chertov et al. 2017b). Some of the SOM transformation models described above are summarized in Table 1. Reliable quantitative evaluation of the formation of HSs using, e.g., the parameters such as HI, HD, and HR requires data from long-term experiments which are lacking because they are usually costly and time consuming. Therefore, mathematical modeling may be considered as a tool for predicting SOM humification dynamics and consequently the formation of HSs from the diverse sources. On the other hand, reliable models require input data preferably from a long-term experiment. To overcome this inconvenience, the existing archival data from a long-term experiment may be used to build and calibrate the mathematical model of SOM humification for this particular case. Then the developed model needs to be validated based on data obtained from the running short-term test to evaluate quantitatively HS formation in this new case.
Table 1

Summary of studies reporting SOM transformation models

Model

C pools

Type

References

PAPRAN

Fresh

Comprehensive

Penning de Vries 1982

Stable

NCSOIL

Residue pool

Comprehensive

Molina et al. 1983

Pool labile

Pool I resistant

Pool II labile

Pool II resistant

Pool III (stable humus)

CENTURY

Structural

Summary

Patron et al., 1992

Metabolic

Active SOM

Slow SOM

Passive SOM

CANDY

Fresh organic matter

Summary

Franko et al. 1995

Biologically active SOM

Slow cycling SOM

SOCRATES

Decomposable plant material

Summary

Grace and Laad 1995

Resistant plant material unprotected MB

Protected MB

Stable OM

RothC

Decomposable plant material

Summary

Coleman and Jenkinson 1996

Microbial biomass

Resistant plant material

Humus

Inert organic matter roots

Litter

Faces

SOMM

Undecomposed litter

Analytical

Chertov and Komorov 1996, 1997

Litter impregnated by humic substances

Humic substances of mineral top soil

ICBM

Young

Analytical

Andren and Katterer 1997

Old

ECOSYS

Plant residue

Comprehensive

Grant 2001

Animal manure

Particulate (active)

Non-particulate (passive) organic matter

Microbial residue

ROMUL

Stable SOM

Summary

Komarov et al. 2016

4 Sustainability of soil management

Soil is mostly non-renewable, one of the most important parts of the natural environment. While not widely appreciated by most people, soil is actually at the center of nearly all terrestrial processes. All economies as well as high living standards depend on goods and services provided by the environment, mainly the soil. Land and soil are vital for society to meet its needs for food, drinking water, energy, shelter, and infrastructure. Many of society’s environmental challenges, such as climate change, depletion of natural resources, and loss of biodiversity, are related to the use of rural and urban land and soil. Many stakeholders (drinking water and energy producers, distributors, and suppliers; spatial planners, consultants, brownfield developers, and remediation contractors; farmers, foresters, and food companies; citizens; financiers; policy makers and regulators) are involved in making balanced and sustainable land use and land management decisions.

Sustainable soil management means temporal and/or spatial harmonization in the uses of key environmental, social, and economic functions, excluding or minimizing irreversible ones, which is not a scientific but a political task (Blum 2005).

The importance of organic matter in soil is not a recent discovery. The maintenance of SOM levels in arable soils is crucial for sustainable crop production for soil and climate protection and mitigation strategies (Freibauer et al. 2004; Lal 2004b; Janzen 2004, 2005; Bellamy et al. 2005; Johnston et al. 2009; Leithold et al. 2015). Furthermore, SOM is a relevant accumulator and transformer of nutrients (Fageria 2012). Simultaneously, in contrast to soils’ calculated potentials to sequester carbon, arable soils currently lose carbon in many regions (Janssens et al. 2005; Kutsch et al. 2010; Schrumpf et al. 2011). Cultivation of soils usually causes a decrease of the organic matter content. For most soils, a high level of organic matter is maintained only by grass species. In agriculture, increasing soil organic carbon content is often seen as a desirable objective, especially in organic farming (Mader et al. 2002; Lal et al. 2004; Yigini and Panagos 2016), though the benefits of organic C in soil in terms of fertility arise in part from its decay and not from its accumulation (Janzen 2004). Sequestration of C in soils and low-carbon technologies have also been promoted as a strategy to mitigate the effects of increasing emissions of greenhouse gases in the atmosphere (Lal 2001; Janzen 2004; Minasny et al. 2017).

With regard to soil key functions and services, including carbon sequestration, losses of SOM cannot be accepted. Therefore, tools for a reliable assessment of the state of exogenous organic matter supply to soils in farming practice are required. Balance of SOM has been approved as a practice-applicable approach to the assessment of SOM management (Brock et al. 2013; Bila et al. 2016). Man has recognized the importance of organic amendments for maintaining or improving the content of SOM since he started farming. Soil fertility in early agricultural systems was based on the recycling of organic wastes. There have been vast changes in the nature of agricultural production. In the past, farms were small and much of what was produced was consumed on the farm. This system allowed for the limited removal of soil nutrients since there was an opportunity to return most of them back to the land. As a result, among others, migration from rural to urban communities, increased farm sizes, and specialization in production have changed a system of production in such a way that there is higher removal of nutrients from the soil and less opportunity for nutrient cycling. In Poland, occurring soil species (predominantly sandy soils) and an inadequate land management have led to reduction of the organic matter contents in soils. This effect is intensified by the limited availability and high cost of conventional sources of applied organic matter such as farm manures or crop residues and the popular use of mineral fertilizers. Therefore, new sources of organic matter have been tapped such as municipal solid waste compost, brown coal of low energetic value, sewage sludge, biochar, and brown coal preparations (Drozd 2003; Kwiatkowska et al. 2008; Sohi et al. 2010; Leszczyńska and Kwiatkowska-Malina, 2011; Usman et al. 2012; Ouyang et al. 2014; Novotny et al. 2015).

Concerning the nutrient cycling, there is a potential conflict in terms of the management of SOM. One objective may be to increase soil organic and soil carbon levels while the second goal is to ensure adequate N, P, and S nutrition for crops and pastures (Janzen 2006). The utilization of the nutrient pool in SOM requires its mineralization which by definition requires the break down and reduction of the SOM pool in the soil. While this does not necessarily mean that both objectives cannot be met by well-balanced and strategic management, it is a fundamental conflict requiring the development of specialized management strategies. The key would appear to be in the development of management strategies that ensure rapid nutrient cycling and take full advantage of the capacity of legumes to fix N. This immediately brings the system under a potential acidification pressure which needs to be managed. Hence, there is a need for finely balanced management systems.

There is a link between the build-up of SOM, the fixation of N by legumes, and the need for adequate P nutrition (Chan et al. 2010). The impact of SOM on productivity is complex because it affects a range of soil properties, not a single property. It seems to relate very well to the overall effects of soil organic matter on productivity (Watt et al. 2006). SOM can influence a number of soil properties and, therefore, potentially a wide range of soil functions. By influencing a range of functions, the effects of SOM can be substantial. The most practical way to improve soil quality is to promote better management of SOM including soil carbon.

Managing SOM remains a sound basis for maintaining soil in a good condition for optimizing productivity and maintaining the productive capacity of soil in the long term. The development of land management strategies to optimize both the increase of soil organic carbon levels and the recycling of nutrients from SOM needs to be a priority. Soil organic carbon sequestration can make soils a sustainable resource, not a renewable resource (Minasny et al. 2017).

5 Conclusions

Land and soil are vital for society to meet its needs for food, drinking water, energy, shelter, and infrastructure. Soil organic matter (SOM) is responsible for the provision of these functions of soil nowadays and in the future. Although analytical methods are widely used to evaluate changes in soil management or organic carbon turnover, the practical question of the quantity and quality of SOM cannot be answered completely. The development of the parameterization system of SOM quality and quantity could assist more rational and sustainable management of the soils. The effective soil management should include its properties as well as different functions: food production (99%), nutrient and water cycling, storage, filtrating and buffering, biological habitat and gene pool, source of raw materials, climate regulations, physical and cultural heritage, platform for man-made structure. Organic matter in soil has been widely promoted as a key indicator of soil quality, particularly in agriculture. There are several different types of SOM as a result of the way of its formation. Understanding of qualitative and quantitative changes of SOM is crucial for agronomic aspects like nutrient dynamics, structure, and water storage capacity. The fundamental conflict between increasing soil organic carbon and using the mineralization of SOM as a source of nutrients requires further investigation and resolution. The modeling of nutrient cycling across seasons, locations, and soil types would be a one of recommendation. Nowadays, for the qualitative analysis of SOM and humic acids (HAs), the analytical techniques are applied, e.g., HPSEC, NMR, and ESI-FTICRMS. A qualitative understanding of SOM dynamics transformations along with numerical modeling for quantitative assessment of HS formation should be used to develop sustainable soil management. The quantitative analysis is done through the following parameters: humification index (HI), humification degree (HD), and humification rate (HR). For better understanding of transformation of SOM, there are used analytical, comprehensive, and summary dynamic models. Summary models are more suitable for applicative and predictive purposes where essential aspects of comprehensive models are formulated in less detail. Therefore, mathematical modeling may be considered as a tool for predicting SOM humification dynamics and consequently the formation of HSs from the diverse sources. The existing archival data from a long-term experiment may be used to build and calibrate the reliable mathematical model of SOM humification. Then the developed model needs to be validated based on data obtained from the running short-term test to evaluate quantitatively HS formation. Dynamics of SOM transformations is important, especially in the context of stability and efficiency of organic matter sources applied into soil. The soil utility value should be evaluated through the SOM qualitative-quantitative analysis of organic carbon and total nitrogen. Managing of SOM remains a sound basis for maintaining soil in a good condition for optimizing productivity and maintaining the productive capacity of soil in the long term. The development of land management strategies to optimize both the increase of soil organic carbon levels and the recycling of nutrients from SOM needs to be a priority. This should include policy makers and other users as well.

Notes

Compliance with ethical standards

Conflict of interest

The author declares that he/she has no conflicts of interest.

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Authors and Affiliations

  1. 1.Department of Spatial Planning and Environmental SciencesWarsaw University of TechnologyWarsawPoland

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