National Comparison of the Total and Sequestered Organic Matter Contents of Conventional and Organic Farm Soils

CHAPTER ONE

National Comparison of the Total and Sequestered Organic Matter Contents of Conventional and Organic Farm Soils Elham A. Ghabbour*,1, Geoffrey Davies*, Tracy Misiewicz†, Reem A. Alami*, Erin M. Askounis*, Nicholas P. Cuozzo*, Alexia J. Filice*, Jennifer M. Haskell*, Andy K. Moy*, Alexandra C. Roach*, Jessica Shade† *National Soil Project, Northeastern University, Boston, MA, United States † The Organic Center, Washington, DC, United States 1 Corresponding author: e-mail address: [email protected]

Contents 1. 2. 3. 4. 5.

Introduction Origin and Nature of SOC HS Functions and Interrelations Views on HS Agriculture and Soil Health 5.1 Soil Degradation 5.2 Fertility Management 6. Comparison of Organic and Conventional Agricultural Soils for Carbon Sequestration 6.1 Materials and Methods 6.2 Results 6.3 Discussion 7. Conclusions References Further Reading

2 4 5 6 8 8 9 10 11 13 19 28 28 35

Abstract Intensive agriculture has been linked to declining soil fertility and is a known source of greenhouse gas emissions. Decline in soil organic matter (SOM) is of particular concern due to its key role in maintaining soil health. Previous research supports the view that fertility management practices utilized in organic agriculture can maintain and even grow the SOM pool, but fewer studies examine what proportion of SOM is sequestered over long periods of time. Using data from the National Soil Project SOM sequestration

Advances in Agronomy, Volume 146 ISSN 0065-2113 http://dx.doi.org/10.1016/bs.agron.2017.07.003

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2017 Elsevier Inc. All rights reserved.

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between soils from organically and conventionally managed farms from across the United States is compared. Total %SOM ranged from 0.63 to 46.1 for conventional farm samples (mean 7.37) and 0.5 to 88.9 for the organic samples (mean 8.33). %FA ranged from 0.08 to 2.20 (mean 0.26) for conventional and 0.04 to 14.8 (mean 0.65) organic farm soils. %HA ranged from 0.17 to 23.0 (mean 2.85) for conventional and 0.25 to 48.9 (mean 4.1) for organic samples. Mean %humification (i.e., sequestration) was 45.6 for conventional soils and 57.3 for organic. Results presented here support previous assertions that FA are the precursors of HA. With the exception of water retention, comparisons of SOM, FA, HA, and humification suggest that organic farming practices support healthy soils and build and/or or maintain SOM more effectively than conventional farming practices. The data from this study can serve as benchmarks for other soils, and the spectroscopic analytical approaches employed should be useful in tracking the effects of changes in farm soil management practices over space and time.

1. INTRODUCTION Over the last century, agricultural systems have increased dramatically in productivity due to the intensification of agricultural practices (Matson et al., 1997; Reid, 2011). However, in the face of increased yield there is growing concern over the declining fertility of soils (Haynes and Naidu, 1998). As a result, land degradation has been identified as a critical issue for the 21st century due to its impacts on decreased agronomic productivity, environmental degradation, and reduced food security (Amundson et al., 2015; Eswaran et al., 2001). Of particular concern is the trend of widespread decline of soil organic matter (SOM) concentrations (Metz et al., 2007). SOM is one of the most important components of healthy soils. It plays a key role in nutrient retention and storage (Russell, 1973; Woomer and Ingram, 1990), water holding capacity, porosity, infiltration capacity, hydraulic conductivity, and waterstable aggregation and decreased bulk density (Davies and Payne, 1988; Haynes and Naidu, 1998; Khaleel et al., 1981; Lavelle, 1988). As a result, soils high in SOM support healthier crop growth, are less susceptible to drought, runoff, and erosion, and support greater soil organismal biodiversity (He and Wu, 2015). SOM is also important for global climate change mitigation as a source of long-term carbon sequestration (Kane, 2015). Increases in SOM content are largely dictated by organic matter inputs such as organic residues and roots and litter decomposition (Leo´n and Osorio, 2014). The rate at which soil organic carbon (SOC) is depleted in the soil is driven by numerous physical, chemical, and biological factors,

Comparison of SOM in Organic and Conventional Soils

3

including moisture, temperature, aeration, nutrient availability, mixing by soil macrofauna, leaching by water, and SOM stabilization. As such, SOM concentrations in soil systems are the consequence of a balance between SOM input and decomposition. When soil SOM stores are depleted, soil restoration must maximize the recycling of SOM and plant nutrients while simultaneously minimizing SOM losses via leaching, runoff, and erosion. Several agricultural practices have been shown to affect SOM levels, for example, soil perturbations, such as soil tillage, which accelerates SOM decomposition and exposes the soil to wind and water erosion which leads to SOM loss (Follett and Schimel, 1989; Nye and Greenland, 1960). The clearing of natural vegetation has also been linked to decreased SOM levels (Srivastava and Singh, 1989), as a lack of crop residue returns to the soil (Bouwman, 1990; Lal, 1986; Post and Mann, 1990; Woomer and Ingram, 1990). Agricultural practices based on minimum tillage (Dossou-Yovo et al., 2016), use of cover crops and crop rotations, and use of organic fertility treatments such as manure and legumes (Haynes et al., 1991; K€ ohn, 1975; PlazaBonilla et al., 2016; Triberti et al., 2016) recycle organic matter back into the soil (Nuttall et al., 1986). Because many of these practices which are known to maintain or increase SOM are central to organic agriculture, it has been posited that soils on organic farms should exhibit higher levels of SOM than soils from conventional agricultural systems (Gattinger et al., 2012). SOM consists of two main pools: one pool termed labile is accessible as food for microbes and includes, but is not limited to, leaf litter and dissolved organic matter (He and Wu, 2015). The other pool called humic substances (HS) is closely associated with soil health (Stevenson, 1994; Sutton and Sposito, 2005). Humic acids (HA) and fulvic acids (FA) are main components of HS in most soils, with HA usually predominating. HS are a particularly important component of SOM because they are thought to be long-lived sequesterers of carbon. Because they are persistent, FA and HA content data provide a baseline that defines the influence of SOM on soil characteristics and properties. Knowledge of FA and HA contents also can facilitate carbon cycle modeling (National Soil Carbon Network, 2012; S€ undermann et al., 2015), where the proportions of labile and HS pools need to be known (Post et al., 1982; Stevenson and Cole, 1999). Furthermore, the HS level reflects the long-term ability of the soil to remain healthy and productive and, as such, HS is the baseline measure of any SOM. HS, and HA in particular, are better water retainers than SOM on an equal mass basis and thus help to combat drought (Ghabbour et al., 2013). Water retention is associated with carboxylic acid groups in HS

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(Sasaki et al., 2007). Their ability to retain water will be especially important in the face of climate change. Thus, it is increasingly important to replenish HS in degraded arable soils. Because the two main soil organic component pools have very different soil mean residence times, the labile pool is turned over much more rapidly than the HS pool (He and Wu, 2015). We can draw four main conclusions: (1) quantifications of SOM are likely to vary across time and space based on climactic and seasonal fluctuations in the labile pool; (2) longer-lived, HS measurements will provide a steady baseline for soil characterization that can be monitored over time and space; (3) by measuring total SOM and total HS, the percentage of SOM that is sequestered in the form of HS can be derived; and (4) because HS levels are steady over time, they can be considered a measure of soil quality and health. This chapter explores the nature and role of HS in soil health, the role of agricultural production in restoring and depleting our soils, and presents data on the SOM, HA, and FA content of soils collected from hundreds of organic and conventional farms across the United States to understand how farming practice influences the carbon sequestration ability of soils.

2. ORIGIN AND NATURE OF SOC The ultimate source of life on Earth is photosynthesis, in which water and CO2 are converted to carbohydrates (mainly glucose) and elemental oxygen. Carbohydrates are linked end to end to form polysaccharides, important examples of which are cellulose (the structural units of plants) and starch (the form in which plants store energy). When a plant dies and returns to the soil, it decomposes into two main carbon pools. One pool is accessible to soil microbes as food and a source of energy: this pool ultimately returns to the atmosphere through soil respiration to complete the carbon cycle. The other pool is protected from microbes and is much longer lived and thereby sequestered (Campbell et al., 1967; Paul et al., 1997, 2001). This pool, once referred to as humus but now called HS, varies from pale yellow to orange to brown or black and is largely responsible for the color of soil (Schulze et al., 1993). HS are intermediates between plants and, in the very long term, coal. HS can be fractionated into three component groups based on aqueous solubility. Yellow-orange FA are soluble in acidic, neutral, and basic aqueous solutions, which means they are mobile in soil profiles. By contrast, brown HA precipitate from acidic solutions (pH <2) and bind to soil

Comparison of SOM in Organic and Conventional Soils

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minerals. The third HS component called humin (HU) is insoluble in all aqueous solutions and believed to be HA that are very firmly attached to mineral surfaces. The link between plants and HS has been demonstrated by isolating HS from living land and ocean plants; one theory is that some plants produce HS in the twilight zone between life and death as a result of stress (Ghabbour et al., 2004a).

3. HS FUNCTIONS AND INTERRELATIONS HS have not been crystallized and therefore their detailed molecular structures are unknown. However, the numerous valuable attributes they provide to soil are well known and include water filtration, purification and retention, soil structure, and aggregate stabilization, and nutrient and toxic element binding are due to their many and varied functional groups. They have been labeled “nature’s most versatile materials” (Ghabbour and Davies, 2004). It recently has been confirmed that HA are assembled from FA (Ghabbour et al., 2015). The resulting HA are larger molecules than FA and have different solubility and light-absorbing characteristics as reflected by the different colors of soils. Radiocarbon dating confirms that HS can have soil residence times of hundreds and thousands of years (Campbell et al., 1967; Paul et al., 1997, 2001); however, they are not immune from conversion to CO2 and eventually return to the atmosphere. Otherwise, the Earth would be covered by HS soup. FA and HA are unique in nature as they exist as stable free radicals (molecules with an odd number of electrons) associated with their quinone functional groups (Lovley et al., 1996; Scott et al., 1998). These groups are subject to reduction by microbes, forming semi-quinone radicals that can be detected and quantified by a technique known as electron spin resonance spectroscopy (Scott et al., 1998). The semi-quinone radicals are converted back to the parent quinones by reaction with mineral-bound Fe(III), which, in turn, is converted to iron(II), an essential plant and microbe nutrient. In this way HA act as electron shuttles between mineral-bound iron and microbes. HA are immobilized on mineral surfaces, especially on clays. This prevents them from being washed out of soils. Studies of the common clay kaolinite have shown that more than one HA layer can be attached on a clay surface (Ghabbour et al., 2004b). Solid HA also very tightly bind two other essential nutrients, Mg2+ and Ca2+ (Ghabbour et al., 2001). A number of important soil processes occur in which nitrogen fertilizer compounds react with and are stored by HS. As an example, ammonia

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(NH3) and ammonium nitrate (NH4NO3) applied to agricultural soils as nitrogenous fertilizers may react chemically with the SOM in what is known as ammonia fixation. Under conditions of heavy application of ammonia to soils, nitrite ðNO2
Þ can accumulate due to nitrification inhibition and also become fixed by HS (Thorn and Mikita, 2000). The reaction of nitrite with HS in the process called chemodenitrification can lead to the formation of nitrogen gases, including nitrous oxide (N2O), a greenhouse gas, and scavenger of stratospheric ozone. Many nitrogenous agrochemicals, fertilizers, herbicides, pesticides, dyes, explosives, and other industrial chemicals in soils may be converted to aromatic amine compounds, which are relatives of ammonia. There is evidence that aromatic amines form covalent bonds with HS, either through abiotic condensation reactions or xenobiotic coupling reactions (Thorn et al., 1996). The stable natural isotope nitrogen-15 is used to follow the nitrogen cycles in soil. 15 NH4 + is strongly absorbed by SOM as shown in a weeklong incubation experiment. Measures of 15N immobilization from mineralization data indicate that applied nitrogen fertilizer is rapidly converted to relatively stable forms during decay of organic residues in soils. At the end of the first growing season 10% to 40% of the 15N initially applied to the soil was present in organic forms. Only 15% of the residual 15N is available to plants during the second growing season, and 15N availability decreases even further with succeeding crops (Stevenson and Cole, 1999). HS are multifunctional in soils: they (1) improve fertilizer efficiency (Leo´n and Osorio, 2014); (2) bind and help retain nitrogen-containing and other nutrients; (3) improve nutrient uptake, especially of phosphorus and calcium; (4) stimulate beneficial soil life; (5) provide magnified nutrition for reduced disease, insect, and frost impact; (6) manage salinity by buffering plants from sodium; (7) catalyze SOC formation; and (8) retain water and buffer soil acidity, thereby inhibiting plant toxins such as aluminum (Bot and Benites, 2005).

4. VIEWS ON HS The centuries-held view of HS as long-lived carbon sequesterers in soils has been challenged by Kleber (2010) and Kleber and Johnson (2010), who have offered an alternative soil continuum model (SCM). This SCM seems at least in part to be due to the conclusion of Kelleher and Simpson (2006) from two-dimensional solution-phase nuclear magnetic resonance spectroscopy that soil is a mixture of decomposing cutins, lignin,

Comparison of SOM in Organic and Conventional Soils

7

polysaccharides, and proteins and has no distinct structures attributable to HS. Lehmann and Kleber (2015) repeat that extraction of HS from soils with even dilute aqueous sodium or potassium hydroxide gives products called HS that do not exist in soils. Using this well-established method, we have achieved reproducible yields of FA and HA from a very wide range of soil types from across the United States. In a well-expounded defense of HS as distinct soil components, Schnitzer and Monreal (2011) have reviewed literature evidence that pyrolysis-field ionization (py-FI) mass spectra of a whole soil and its FA, HA, and HU fractions extracted with 0.5 M NaOH at room temperature were indistinguishable, indicating that extraction of soils with dilute aqueous NaOH has no discernable structural effect. In support, Piccolo (2016) and Hayes and Swift (2017) point out that an understanding of any biomaterial (e.g., a protein, DNA) requires its isolation from the matrix, and alkaline extraction of soils has been applied for centuries as the most efficient extractor of HS from soils (Hayes and Swift, 2017). Schnitzer and Monreal (2011) present data showing that the compound classes assigned to py-FIMS of the FA and HA of the Bainsville soil are very similar in classification and relative amounts, indicating that FA and HA are structurally linked. Stolt et al. (2016a) concluded from decreasing FA/HA mass ratios with increasing SOM humification that FA are precursors of HA. Lower H/C atomic ratios for HA than for FA indicate that humification results in aromatization of aliphatics and intensification of soil color (Schulze et al., 1993; Zech et al., 1992). As further evidence for the existence of HS as distinct soil components, the absorbance, fluorescence, and photochemistry of HS have properties that are not consistent with the superposition of individual chromophores as in the SCM of Kleber and Johnson (2010). These include (1) featureless optical absorbance that decreases exponentially with increasing wavelength well into the visible region (Ghabbour and Davies, 2009); (2) fluorescence emission spectra that fall in a single envelope that is independent of the excitation wavelength; and (3) photobleaching and photochemical quantum yields that decrease exponentially with increasing wavelength (Sharpless and Blough, 2014). These properties are consistent with intramolecular charge transfer between electron donor and acceptor groups in HS molecules arising from the presence of aromatic chromophores. Charge transfer occurs between triplet states of hydroxyl/methoxy aromatic electron donors and carbonyl-containing electron acceptors (quinones and/or aromatic ketones or aldehydes) (Sharpless and Blough, 2014).

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5. AGRICULTURE AND SOIL HEALTH 5.1 Soil Degradation The world population is expected to increase to 9.5 billion by 2050 and with it the demand for food. Soils are a finite resource and their widespread degradation threatens to up-end global food security. Soil degradation occurs via chemical, physical, and biological processes, resulting in loss of SOM, loss of biodiversity, loss of soil fertility, nutrient imbalance, increased acidity (Graham and Haynes, 2005), and increased salinity (Akramkhanov et al., 2011; Metternicht and Zinck, 2003; Wiegand et al., 1996). Chemical soil degradation includes increased levels of toxic elements such as aluminum, decreased nutrient calcium and magnesium levels, loss of nitrate and other essential plant nutrients, pollution by industrial waste, and SOM loss with rising temperatures (Melillo et al., 2002, 2010). Physical soil degradation includes reduction of structural integrity, leading to compaction, crusting, and loss of water permeability that results in wide soil temperature fluctuations, increased surface runoff, and soil erosion. Biological soil degradation involves loss of SOM, lowered biodiversity, loss of a soil’s ability to store and sequester carbon and release plant nutrients, and the release of greenhouse gases. The overall effect of continued soil degradation resulting from land misuse and mismanagement is steady decrease of soil quality and agricultural productivity. 5.1.1 Soil Erosion Soil erosion is a particularly serious problem worldwide (Boardman, 2006; Manzatto et al., 2002; Orimoogunje, 2014). It is estimated that erosion of agricultural soils in the United States is responsible for loss of an average of 30 tons per hectare per year, about eight times greater than the rate of soil formation in the human lifetime. Soil erosion is accelerated by a marked landscape slope, removal of vegetation to create agricultural land, drought, soil tillage, wind, or water, but erosion by water is the most widespread and serious. This is because the force of gravity on water and ice may lower the shear strength of landscape slopes, making soils behave like plastics or, under very moist conditions, like fluids (Abrahams, 1986; Brunsden, 1988; Clague and Robert, 2012; Goudie and Viles, 1997; Kanungo and Sharma, 2014; Selby, 1993). Factors such as rainfall intensity, rainfall volume, slope angle, soil properties, land use, and land management determine soil erosion rates; however, increasing SOM has been demonstrated to effectively reduce soil erosion by

Comparison of SOM in Organic and Conventional Soils

9

wind and water (Rhoton et al., 2002). Agricultural management practices that are particularly important in reducing soil erosion include reducing tillage (Blanco-Canqui and Lal, 2004; Gelaw et al., 2015) and maximizing ground cover with food crops, plant residues, and perennial cover crops. These practices not only reduce soil erosion but also promote soil formation, carbon sequestration, and nutrient retention. No-till practices have also been demonstrated to reduce soil erosion via wind by reducing the amount of soil macroaggregate turnover. Soils contain organic matter–mineral microaggregates that in favorable circumstances bind together to form macroaggregates (Elliott, 1986). These macroaggregates generally contain higher levels of SOC and nitrogen than microaggregates because of their higher levels of decomposing roots and fungal hyphae (Tisdall and Oades, 1982) and as such are structurally more resistant to fracture or dissolution by water than their microaggregate precursors. Recent studies have shown that sequestered SOM in the form of HS is several times better at retaining water than the total SOM content (Ghabbour et al., 2015). This leads to climate moderation from the high thermal capacity of water and soil density increase which reduces soil erosion by wind.

5.2 Fertility Management Fertilization is an agricultural practice that has been promoted as a means to increase SOM directly or indirectly. Inorganic nitrogen (N) fertilizer may increase crop growth, indirectly augmenting SOM through increased crop residue inputs; however, there is still debate about the effects of inorganic fertilizer on building or degrading SOM. For example, while some studies have found positive effects of N fertilizer on SOM (Davies and Payne, 1988; Haynes and Naidu, 1998; Hyv€ onen et al., 2008; Wang et al., 2015), others have found negative effects (Fr€ oberg et al., 2013; Khan et al., 2007; Manna et al., 2007). Khan et al. (2007) examined change in soil carbon over time in the Morrow Plots, the oldest experimental site under a continuous corn rotation, and found that long-term fertilization of plots with inorganic N fertilizer at rates that exceeded the crop N removal rate led to decrease in soil carbon in spite of incorporating large amount of crop residues into the soil. Declines in carbon were also observed in plots utilizing a corn– soybean and corn–soy–oat rotation. These declines in soil carbon were thought to be driven by overfertilization whereby excess N accelerates decomposition of plant residues and SOC, resulting in no soil accumulation of SOC and sometimes leading to a decrease in SOC.

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Alternatively, application of organic matter is well supported as a means to increase SOC through direct deposition of carbon-rich material. For instance, recent studies have found that while application of inorganic N fertilizers alone does not effectively increase SOC, application of N fertilizer in combination with organic matter such as straw or manure can effectively increase SOC storage (Dou et al., 2016; Jiang et al., 2014; Zhou et al., 2013). Furthermore, several studies have suggested that organic management can increase SOM through use of manure, compost, and legume cover crops which are plowed under to increase soil fertility (Cavigelli et al., 2013; Marriott and Wander, 2006; Spargo et al., 2011; Wander et al., 1994). Some studies have suggested that the observed increase in SOM input will eventually be offset by increased SOM decomposition due to the practice of tillage in organic farming (Arshad et al., 1990; Beare et al., 1994). However, results from the USDA Agricultural Research Service’s Farming Systems Project found that SOC in two-year, three-year, and six-year organic rotations had greater SOM than conventional no-till plots (Cavigelli et al., 2013). Similarly, Teasdale et al. (2007) found that even when tillage was utilized, organic plots exhibited higher levels of soil combustible carbon after 9 years compared with conventional no-till systems that also included cover crops. Together these results suggest that organic practices such as the use of manure, compost, and legumes to augment soil fertility can provide greater long-term soil benefits than inorganic N fertility treatments in spite of tillage.

6. COMPARISON OF ORGANIC AND CONVENTIONAL AGRICULTURAL SOILS FOR CARBON SEQUESTRATION While numerous studies have compared the effects of conventional and organic management, few, if any, studies have systematically compared organically managed soils to conventionally managed soils across the entire United States to determine the impacts of management system on SOM levels. Additionally, while total SOM measurements are frequently used in studies of soil health (Blanco-Canqui and Lal, 2008; Burke et al., 1989; Eswaran et al., 1993; Heiderer, 2009; Oades, 1988; Parton et al., 1987; Tiessen et al., 1994), few have examined the levels of SOM components (HA and FA), especially in the context of agricultural soil health. Humic acids represent the long-term storage of carbon in the soil and are a much more accurate measure of soil health, because they are more closely related with beneficial soil properties such as water retention, nutrient

Comparison of SOM in Organic and Conventional Soils

11

storage, and improved texture and permeability; thus, it is critical to evaluate them individually to get a true measure of long-term carbon storage and soil health. This study uses analytical methods to examine not only the total SOM content of conventional and organic soils but also the levels of HA and FA in the soil. We have examined hundreds of conventional and organic soil samples from across the United States to answer the question: How does organic management affect the levels of SOM component sequestration in soils?

6.1 Materials and Methods 6.1.1 Soil Classification This study adopts the United States Department of Agriculture’s definition of organic as “[A] labeling term that indicates that the food or other agricultural product has been produced through approved methods. These methods integrate cultural, biological, and mechanical practices that foster cycling of resources, promote ecological balance, and conserve biodiversity. Synthetic fertilizers, sewage sludge, irradiation, and genetic engineering may not be used” (USDA, 2012). All “organic” soil samples were collected from USDA-certified organic farms. 6.1.2 Sample Collection and Handling All soil samples from came from the National Soil Project (NSP). The NSP was initiated at Northeastern University in 2008. Soil samples from conventional and agricultural farms were mailed to NSP for analysis of %SOM and the percentages of sequestered FA and HA, a service not previously available. The ability of each air-dried soil sample to retain water, %SOM, % FA, and %HA was quantified on a dried sample basis. Samples were collected from surface (0–30 cm) agricultural top soil in 50 g measurements by local farmers. Leaves, sticks, rocks, pebbles, and trash were removed from the samples. Samples were then air-dried and sent via USPS Parcel Post to the NSP laboratory in Boston, MA. Farmers also included information about geographical location of soil collection, soil texture, and classification. All organic soil samples came from USDA organic-certified farms. Less than 30% of soil samples were submitted without information regarding commodity being produced or farming practices being utilized; therefore, this information was not included in our analysis. Samples were then stored at room temperature in sealed containers until processing.

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Upon arrival, in the laboratory care was taken to ensure no cross contamination among samples. Samples were air-dried and analyzed in order of receipt. The mass of each air-dried sample was measured before and after heating at 105°C for 24 h. The difference in mass was taken as a measure of the sample’s capacity to retain water. All subsequent analyses were conducted with samples dried at 105°C. 6.1.3 Percent Total Soil Organic Matter (%SOM) Total SOM measurements were conducted in a fully programmable NeyVulcan Model 3-550 muffle furnace (interior dimensions 20 cm high, 23 cm wide, and 21 cm deep and heated from both sides). Temperature was controlled to 2°C. Previous work shows that combustion of dried soils at 650°C for 12 h gives reproducible results for a wide variety of samples (Ghabbour et al., 2014). Unlike measurements at lower ignition temperatures and/or shorter ignition times, data under these optimized conditions are not sample dependent and have an average coefficient of variance of 4%. The loss-on-ignition (LOI) for each sample was equated to its % SOM content on a dried sample basis. 6.1.4 HA and FA Fractionation and Measurement This study used novel techniques to examine levels of FA and HA (HA) in the soil, in addition to total SOM contents. Percent total SOM contents (%SOM) are usually measured by high temperature combustion of dried soil samples and recorded either as gravimetric LOI or measurement of the CO2 combustion product (Pribyl, 2010). However, soil combustion analysis does not distinguish between labile, short-lived soil components (e.g., corn stover, grass clippings, straw) (He and Wu, 2015) and soil HS, which carbon-14 dating has shown to be much less accessible to microbes as sources of energy (Campbell et al., 1967; Paul et al., 1997). To address this, the NSP has developed an optimized LOI method of measuring the %SOM in dried soils with wide range of carbonate, clay, sand, and silt (Ghabbour et al., 2014). In a recent study of soils from southern Idaho (Ghabbour et al., 2012), the procedure was optimized by varying the reagent concentrations and reaction times with five test soils at room temperature. The analytical protocol consists of HA and FA fractionation; HA were measured gravimetrically (Ghabbour et al., 2012). Solutions of FA are analyzed from their visible spectra (Ghabbour and Davies, 2009). The extractions were run in parallel to increase sample throughput. Unlike other spectroscopic approaches (e.g., Lamar and Talbot, 2009), this HA analytical method is gravimetric and requires no standards.

Comparison of SOM in Organic and Conventional Soils

13

6.1.5 Water Retention Soil samples were logged in and subjected to an airflow of 14 ft3/min in large porcelain basins in an air-conditioned laboratory hood for 24 h and then dried overnight at 110°C as a measure of their ability to retain water (Ghabbour et al., 2013). 6.1.6 Statistical Analysis All data analyses were performed in R (R Development Core Team, 2008). To examine %FA and %HA contents, %water retention, total %SOM, and % humification (%H) defined in Eq. (1), we calculated means and standard deviations for organic and conventional management groups. We examined Tukey box plots of all variables to check for outliers and spread of the data. We also plotted histograms for the variables and transformed data using a log transformation if the distribution of a variable was not normal   ½%HA + %FA %H ¼  100: (1) %SOM Because the soil variables could be correlated, we used a multivariate ANOVA (MANOVA) to determine whether there were significant differences between variable means due to organic vs conventional soil management. We used a multifactorial model that also included the US State that the sample was taken from as a cofactor, because location can have an impact on soil properties (e.g., Wardle et al., 2004). We also performed Pearson’s correlation analysis on all soil variables to determine correlation coefficients. Because %H was calculated based on HA and FA contents in Eq. (1), we left this variable out of the MANOVA. To determine which variables contributed to significant differences between soil management, we followed the MANOVA with univariate ANOVAs on each soil variable (%FA and %HA contents, %water retention, %SOM, and %H). All significance levels were corrected for multiple tests using Bonferroni corrections.

6.2 Results 659 organic soils and 728 conventional agricultural soils were collected and analyzed for this study (see supplementary material on http://dx.doi.org/ 10.1016/bs.agron.2017.07.003). Conventional farm soils were collected from 594 Counties in 48 US States. The organic farm soils include 138 counties in 38 US States. Percent SOM ranges from 0.63 to 46.1 for conventional farm samples and 0.5 to 88.9 for the organic samples. %FA

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ranged from 0.08 to 2.20 and 0.04 to 14.8 for conventional and organic farm soils, respectively, and the corresponding %HA ranged from 0.17 to 23.0 and 0.25 to 48.9. Percent FA and %HA contents, %water retention, %SOM, and %H were not normally distributed, so we performed a log transformation on the data. Additionally, because some of the variables showed correlation (Table 1), rather than solely conducting individual ANOVAs on each variable, we also conducted a MANOVA. Our MANOVA analysis (Table 2) showed that soil management is significant, as is the State that samples were collected from. Additionally, there was a significant management by State interaction, showing that the effects of soil management differ by the State a soil is collected from. Because the MANOVA showed significant differences between both soil management and States, we also conducted univariate ANOVAs on each soil variable (%HA and %FA contents, %water retention, %SOM, and %H) to determine which variables contribute to these differences. Table 1 Pearson’s Correlation Table Shows That Most Correlations Between Variables Were Around ρ ¼ 0.5 %HA %FA %Water Retention %SOM

Humic acid

1

Fulvic acid

0.50

0.41

0.72

1

0.16

0.62

1

0.50

Soil moisture SOM

1

The correlation between %SOM and %HA contents was the highest (ρ ¼ 0.72), followed by the correlation between %SOM and %FA (ρ ¼ 0.62), %SOM and % water retention (ρ ¼ 0.50), and %FA and % HA (ρ ¼ 0.50). The lowest correlation was between %FA and % soil water retention (ρ ¼ 0.16).

Table 2 The MANOVA Analysis Showed That Soil Management and State Were Highly Significant, as Was the Interaction Between the Two Factors Df Pillai F Value Num Df Den Df Pr (>F)

Management

1

0.39

State

55

1.17

Management  State

35

0.29

Residuals

1141

***Significance codes: <0.001.

4

1138

<0.001***

8.24

220

4564

<0.001***

2.58

140

4564

<0.001***

181.8

15

Comparison of SOM in Organic and Conventional Soils

6.2.1 Humic Acids Percent HA means and standard deviations (sd) for organic and conventional groups were 4.11 (sd ¼ 3.52) and 2.85 (sd ¼ 2.31), respectively (Fig. 1). Organic management had a wider spread of data with two distinct upper outliers. These two outliers with extremely high %HA contents (48.9 and 37.9) were found in Lenawee County, Michigan, and were also outliers for high %FA and %SOM. These samples are muck soils, defined as soils rich in %SOM and %HS derived from drained swamps. Interestingly, there are a total of four soil samples from Lenawee County, and while two of them were outliers, the other two were well within the 1.5 interquartile range of the upper and lower quartiles. This points to the need for fine-scale testing or common garden experiments, as it suggests large-scale changes in soil properties over short distances. ANOVA analysis of the data showed that organic soil was significantly higher in %HA than conventional soils (P < 0.001), even when the cofactor of the State in which the soil was collected was controlled for. Management, State, and management by State interactions were all significant (P < 0.001) (Table 3). 6.2.2 Fulvic Acids FA contents had a mean and standard deviation of 0.65 (sd ¼ 0.97) for organic and 0.26 (sd ¼ 0.25) for conventional samples (Fig. 1). According to our ANOVA, %FA was significantly higher in organic soil than A

4

B

C

0.6

7.5

Mean %

3 0.4

5.0

1

0.2

2.5

0

0.0

2

Mean %

D

E

0.0 Management

6

60

4

40

2

20

0

0

Conventional Organic

Management

Fig. 1 Mean levels of (A) humic acid percentage, (B) fulvic acid percentage, (C) soil organic matter percentage, (D) water retention percentage, and (E) percent humification.

16

Elham A. Ghabbour et al.

Table 3 ANOVA Data for HA Df

Sum Sq

Management

Mean Sq

F value

Pr (>F)

149.28

<0.001***

1

62.2

61.15

State

55

203.6

3.7

9.04

<0.001***

Management  State

35

42.9

1.23

3.00

<0.001***

1141

467.4

0.41

Residuals ***Significance codes: <0.001.

Table 4 ANOVA Data for FA Df

Sum Sq

Mean Sq

F value

Pr (>F)

1

188.8

188.8

371.46

<0.001***

State

55

347.7

6.32

12.44

<0.001***

Management  State

35

43.1

1.23

2.42

<0.001***

1141

579.9

0.51

Management

Residuals ***Significance codes: <0.001.

Table 5 ANOVA Data for SOM Df

Sum Sq

Mean Sq

F value

Pr (>F)

1

6.5

6.48

22.36

<0.001***

State

55

120.2

2.18

7.54

<0.001***

Management  State

35

19.9

0.57

1.96

<0.001***

1141

330.5

0.29

Management

Residuals ***Significance codes: <0.001.

conventional soil (P < 0.001), with soil management, State of origin, and management by State interaction all being significant (Table 4). 6.2.3 Percent SOM %SOM had means and standard deviations of 8.33 (sd ¼ 6.83) for organic and 7.37 (sd ¼ 5.36) for conventional samples (Fig. 1). %SOM was significantly higher in organically managed soil than conventionally managed soils (P < 0.001). Again, soil management, soil collection State, and soil management by State interactions were all significant (Table 5).

17

Comparison of SOM in Organic and Conventional Soils

6.2.4 Percent Water Retention Water retention had means and standard deviations of 3.23 (sd ¼ 2.95) for organic and 5.78 (sd ¼ 5.68) for conventional soils (Fig. 1). ANOVA analysis of the data showed that soil management and State were both significant factors (P < 0.001), along with a management by State interaction (P ¼ 0.006), with conventional soil having significantly higher %water retention than organic soils (Table 6). 6.2.5 Percent Humification %H had means and standard deviations of 57.3 (sd ¼ 18.7) for organic and 45.1 (sd ¼ 22.8) for conventional management (Fig. 1). Organic soil had significantly higher humification levels than conventional soil (P < 0.001). In addition, differences in management, soil collection State, and the interaction between management and State were significant (P < 0.001) (Table 7). 6.2.6 Graphical Insights Further perspectives on the benefits of organic farming management practices can be gained from the following graphical displays. Fig. 2 shows the Table 6 ANOVA Data for Water Retention Df Sum Sq

Mean Sq

F value

Pr (>F)

Management

1

33.6

33.64

57.32

<0.001***

State

55

206.9

3.76

6.41

<0.001***

Management  State

35

34.2

0.98

1.67

0.009***

1143

673.9

0.59

Residuals ***Significance codes: <0.001.

Table 7 ANOVA Data for %Humification Df Sum Sq

Mean Sq

F value

Pr (>F)

Management

1

33.58

33.58

203.7

<0.001***

State

55

67.11

1.22

7.4

<0.001***

Management  State

35

19.42

0.55

3.37

<0.001***

1141

188.09

0.16

Residuals ***Significance codes: <0.001.

18

Elham A. Ghabbour et al.

A

B 400

Conventional

60

Organic

Organic

50

300 % of Samples

Number of samples

350

Conventional

250 200 150

40 30 20

100 10

50 %SOM

0 0

10

20

30

%SOM

0 0

10

20

30

Fig. 2 Number of samples vs total SOM content. Both curves have a maximum at 6% SOM, but there are 27 more organic samples than conventional at this 6% level. Above 6% the SOM levels are comparable.

number of samples with a particular %SOM and the corresponding percentage of the total samples for organic and conventional farm soils. The curves in Fig. 2A have a maximum at about 6% SOM, but the maximum in Fig. 2B is about 8% greater for organic than for conventional samples. The curves for number and % of total number of samples for organic and conventional samples cross at 6% HA in Fig. 3. There are more conventional soils with HA below 6% than for organic soils and more organic soils with HA >6% than for conventional soil samples. In recent work with a variety of soils we have found that the mass ratio FA/HA decreases with increasing humification in Eq. (1) (Ghabbour et al., 2015; Stolt et al., 2016b). Fig. 4 illustrates this trend for all the conventional and organic samples of this study, supporting the conclusion that FA are the precursors of HA (Sugahara and Inoko, 1981). Data above the power function trend lines in Fig. 4 are for soils in which the FA ! HA conversion is less complete due to the soil conditions in which it takes place. Thus, the FA vs HA plots in Fig. 5 show that FA is, on average, four to five times more prevalent in organic soils than conventional. Assuming similar FA ! HA conversion rates, it takes longer to convert all the FA to HA at higher FA levels. Because of the wide variety of samples studied here, we could not graphically discern the three- to fivefold greater ability of %HA vs %SOM to retain water in soils of similar provenance as shown in Fig. 6 (Ghabbour et al., 2013, 2015).

19

Comparison of SOM in Organic and Conventional Soils

A

B 700

Conventional

Conventional Organic

90

Organic

600

100

80 70 % of Samples

Number of sample

500

400

300

60 50 40 30

200

20 100

10

%HA 0

0

5

10

15

%HA 20

0 0

5

10

15

20

Fig. 3 Number of samples vs HA content for organic and conventional samples. The curves cross at 6% HA. Below 6% there are many more conventional samples than organic. Above this 2.5% level, organic samples always exceed conventional in HA content, especially in the 2.5%–5% range.

6.3 Discussion Our results support previous studies that demonstrate that organic farming techniques lead to higher SOM sequestration than conventional farm management. %SOM ranges from 0.63 to 46.1 for conventional farm samples and 0.5 to 88.9 for the organic samples. The average %SOM is 7.37 for conventional and 8.33 for organic samples. %FA ranges are 0.08 to 2.20 and 0.04 to 14.8 for conventional and organic farm soils with mean values of 0.26 and 0.65, respectively. The %HA ranges are 0.17 to 23.0 (mean 2.85) for conventional and 0.25 to 48.9 (mean 4.1) for organic samples. The mean % humification (i.e., sequestration) is 45.6 for conventional soils and 57.3 for organic. %SOM and %HA were found to be closely related. Based on previous work and the observations made here, FA are the precursors of HA. Except for water retention, which is statistically better in conventional soils, all other comparisons fall in favor of organic farm soil samples. While to our knowledge no other study has quantified and compared SOM, HA, and FA among soils from organic and conventionally farms, numerous other studies have compared SOC among conventionally and

20

Elham A. Ghabbour et al.

organically managed systems and found that organic practices including the use of cover crops, long crop rotations, and the use of manure and legumes for fertility result in greater SOC levels in organic systems as compared to conventional (Aguilera et al., 2013; Cavigelli et al., 2013; Delate et al., 2013; Lazzerini et al., 2014; Pimentel et al., 2005). A number of long-term comparison studies have compared SOC levels among organically and conventionally managed plots. The Rodale Institute Farming System Trial, an ongoing long-term study established in 1981, compares a conventional cropping system, an organic cropping system that utilizes a combination of manure and legume fertility treatments, and an organic system that utilizes legumes only for fertility treatment. At the initiation of the study SOC levels did not differ across the three systems. When SOC was measured again in 2002, a significant increase in SOC was observed among all plots with the greatest increase in the organically managed plots (2.5%, 2.4%, and 2.0% increase, respectively). Furthermore, while the carbon input in the manure– legume organic system was 12% higher than the inputs into the organic legume and conventional systems, which did not differ significantly from each other, both organic systems retained significantly more carbon in their soils with annual increase of 981 and 574 kg C ha
1 for the two organic systems as opposed to 293 kg C ha
1 in the conventional system (Pimentel et al., 2005). A similar result was observed from the Long-Term Agroecological Research (LTAR) experiment in Iowa where the conventional corn–soy rotation was found to have significantly less SOC than the organic corn– soy–oat/alfalfa rotation or the organic corn–soy–oat/alfalfa–alfalfa rotation (22.9, 24.4, 24.0 g/kg, respectively) (Delate et al., 2013). Finally, the USDA Agricultural Research Service’s Farming Systems Project (FSP), which was established in 1996, compares a 2-year, 3-year, and 6-year organic rotation and two conventional cropping systems—one utilizing no-till and another utilizing chisel tillage—and found that SOC in all organic systems was greater than in the conventional no-till system (Cavigelli et al., 2013). Gattinger et al. (2012) conducted a meta-analysis of published data from over 70 different studies and found that SOC concentrations, soil carbon stocks, and carbon sequestration rates were significantly higher in agricultural soils under organic management compared to those under conventional management. In order to account for SOC increases attributable to application of off-farm sources of manure for fertilization, a subset of data from organic farms with zero net input was analyzed as well. Even with this conservative data set, SOC stocks and carbon sequestration were still higher under organic management.

21

Comparison of SOM in Organic and Conventional Soils

1.0

FA/HA

Organic soil

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

%H 0

1.0

20

40

60

FA/HA

80

100

120

140

Conventional soil

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

%H

0.0 0

20

40

60

80

100

120

Fig. 4 FA/HA vs %humification. The ratio FA/HA decreases with increasing %humification, as observed with NEON and aquod soil samples. This confirms that FA are precursors of HA. Points above the power function curve are due to lower FA ! HA conversion.

The data set presented here further supports the soil management as a way to sequester carbon over long periods of time. Our results are in line with previous research demonstrating that organic farming supports higher levels of %SOM than conventionally managed agricultural soils, but and add to this body of knowledge by suggesting that a larger portion of SOM in organically managed soils can contribute to long-term carbon sequestration and hence climate change mitigation.

22

Elham A. Ghabbour et al.

6

Organic soil

FA, %w/w

5

y = 0.19x – 0.15 R2 = 0.50

4 3 2 1 0

0

5

10

15

20 HA, %w/w

6

Conventional soil

FA, %w/w 5

y = 0.04x + 0.16 R2 = 0.14

4 3 2 1 0

0

5

10

15

20 HA, %w/w

Fig. 5 FA vs HA. The different slopes for organic vs conventional samples indicate different average extents of FA ! HA conversion: 96% for conventional and 80% for organic.

The data from this study can serve as benchmarks for other soils and the analytical approaches employed should be useful in tracking the effects of changes in farm soil management practices over space and time. 6.3.1 Modern Approaches to Measuring SOC As interest in the ability of soils to mitigate climate change has increased, so has the international community’s attempt to accurately estimate carbon sequestration. As such, spectroscopy has become a common means to estimate SOC levels. Visible-near-infrared (VNIR) and mid-range infrared reflectance spectroscopies are two alternative approaches, with VNIR being

B

50

Organic soil

HA

40

Retained water, %w/w

Retained water, %w/w

A

y = 0.621x + 0.7136 R 2 = 0.4983

30 20 10

50

Organic soil

40 y = 0.3319x + 0.4727 R 2 = 0.5744

30 20 10

HA, %w/w 0

0

20

40

60

80

HA, %w/w 0

100

C

0

20

40

60

80

100

D 50

Conventional soil

40 30

y = 0.7322x + 3.6878 R 2 = 0.0881

20 10 0

HA, %w/w 0

20

40

60

50

HA Retained water, %w/w

Retained water, %w/w

SOM

80

100

Conventional soil

SOM

40 y = 0.301x + 3.5574 R 2 = 0.0802

30 20 10

HA, %w/w 0

0

20

40

60

80

100

Fig. 6 Water retention vs HA and SOM. (A, B) For the organic samples, R2 is six to seven times larger than for (C, D) conventional, reflecting more uniform soil management. In both cases, HA retain about double the water retained by total SOM, as observed for Maine, NEON, and aquod soil samples.

24

Elham A. Ghabbour et al.

the simpler in practice. Two considerations arise in this effort. One is the compositional and spatial complexity of soils and the lack of a universal soil standard. The other is that soil VNIR reflectance spectra are essentially featureless in the range from 350 to 800 nm, which encompasses the visible and earliest IR region. Despite these considerations, soil spectroscopy has the capability to track SOC contents with regard to land management effects on soil health and productivity in the long term. Pertinent to climate change is the fact that soils are by far the largest sequesterers of atmospheric carbon (Metz et al., 2007). Spectroscopists using instruments that measure soil spectral reflectance typically reduce the data to 10 nm bands to match the instrument resolution. The band data are the input for various preprocessing (e.g., smoothing with the Savitzky–Golay filter (1964)) and spectral interpretation approaches (see, e.g., Vasques et al., 2008, 2009; Yang and Li, 2013). The success of these approaches is evaluated with the aid of statistical parameters such as R2 (the coefficient of determination) and RPD (the ratio of the standard deviation of the observed values to the root mean squared error of the predictions, RMSE). Three decades of soil VNIR research have led to a good understanding of its interpretive power and limitations (Arachchi et al., 2016; Viscarra Rossel et al., 2016). Several publications associate reflectance bands in the visible region of a soil sample with its SOC content. Thus, Vasques et al. (2008) studied fractionated north Florida soils and concluded that a band located near 400 nm is most closely associated with SOC. Viscarra Rossel et al. (2008) demonstrated the use of a hand-held digital camera to measure soil color. They developed functions for predicting SOC contents and compared their predictions with those indicated by partial least squares regression (PLSR) of the VNIR spectra (400–1100 nm). Most of the data used for the SOC content predictions were in the visible region, in support of Vasques et al. (2008). In the process of building spectral libraries, Viscarra Rossel and Webster (2012) used a tree-based analytical approach to extract 24 soil properties of 20,000 archived Australian soils. The wavelength bands used in the conditions of the rule set that predicts SOC were at 510, 610, 630, and 690 nm. Stevens et al. (2013) have investigated 13 chemical and physical properties of 20,000 soil samples from 23 European countries. Local samples were used exclusively for spectral calibration. SOC was measured by dry combustion. These authors used principal component analysis (PCA), PLSR, boosted regression tree, random forest, radial-basis support vector machine regression, multivariate adaptive regression splines, and Cubist (Minasny and

Comparison of SOM in Organic and Conventional Soils

25

McBratney, 2008) for the analysis of the spectra and concluded that the 620 nm feature was associated with various organic compounds. Yang and Li (2013) studied samples of brown calcic soils of the northern Tianshan Mountains of China. They confirmed that quantitative analysis of soil using VNIR requires sophisticated statistical techniques to determine the optimum bands with the greatest sensitivity to SOC. The best statistical model for SOC prediction came from multivariate statistical methods. SOC has a stronger correlation with the second derivative of reflectance spectra than with the original reflectance and other data transforms. Use of the full VNIR spectral range gave better SOC predictions than using visible spectra separately. For preprocessing they used transformation of reflectance spectra to reduce spectral nonlinearities, spectral normalization using multiplicative spectral correction to allow for light scattering (Geladi and Kowalski, 1986), random noise reduction and signal-to-noise ratio improvement using the Savitzky–Golay filter (Savitzky and Golay, 1964), and spectral resolution enhancement and background elimination with first-derivative and mean center function applications (Viscarra Rossel et al., 2006). In addition to direct analysis of soil reflectance, Yang and Li (2013) used first and second derivatives and 10 other data transforms. Reflectance transformation linearizes the correlation between reflectance and soil physicochemical properties. Spectral data were processed using correlation analysis, multivariate stepwise linear regression, and curve estimation to build SOC content prediction models. Continua were removed and the spectra were concave from 400 to 750 nm. Reflectance decreases with increasing SOC. The decrease was especially noticeable from 750 to 600 nm, as also reported by Nocita et al. (2011), who found the highest correlation between SOC and reflectance at around 600 nm. Reflectance increases with increasing wavelength in the 550–800 nm range and the eigenvalue curve changes from concave to convex. So SOC mainly affects reflectance in the 400–700 nm range (Gomez et al., 2008). The maximum R2 for reflectance–second-derivative SOC correlation was found at 444 nm. An exponential relationship between SOC and reflectance was noted. A change of slope of the correlation coefficient was observed in the visible region (see Fig. 3 of Yang and Li, 2013). The Yang and Li work resulted in the relation SOC (predicted) ¼ 0.870 SOC (measured) + 0.033 (R2 ¼ 0.894, RMSE ¼ 0.322). Viscarra Rossel and Hicks (2015) studied soils and their fractions, namely, particulate organic carbon (POC), humic OC, and resistant OC from Australia’s Soil Carbon Research Program with VNIR spectroscopy.

26

Elham A. Ghabbour et al.

They analyzed the spectra with a decision tree approach. High SOC leads to a concave spectral shape in the 400–1000 nm range and a change of slope near 500 nm (see Figs. 3 and 6, respectively, of Viscarra Rossel and Hicks (2015)). Eighteen visible and 17 NIR wavelength data were used to estimate humic OC. These authors concluded that spectral transfer functions depend on the soil used to generate them (i.e., local soils should be used for local soil calibration). Viscarra Rossel et al. (2016) have embarked on the worthy goal of creating a global soil spectral library. To date it encompasses eight countries in Africa, Asia, Australia, Europe, and North and South America. It has 23,631 spectra from 35 institutions and spectra donations are encouraged. Spectral soil composition is associated with land cover and geographical distribution as surrogates for global climate variability. Soil organic and inorganic C, clay, silt, sand, Fe, cation exchange capacity, and pH are estimated (Soriano-Disla et al., 2014). Spectral analysis is with the Cubist machine learning algorithm that results in a derived harmonized global soil attribute data set. Two components are discernable in the visible region (see Fig. 1 of Viscarra Rossel et al., 2016). From samples analyzed to date the average SOC is 2.16% (positively skewed) and the median is 1%. This study defines R2 ranges of usefulness and RPDs. A wavelength of 640 nm is associated with SOC. A change of reflectance spectral slope at around 570 nm is discernable in the top panels of Fig. 10 of Viscarra Rossel et al. (2016). This multi-author study establishes more SOC in cooler, wetter climates at mid-latitudes and reports average SOC by continent in its Table 10 of Viscarra Rossel et al. (2016). The authors surprisingly conclude that spectroscopic predictions are more accurate than lab data. Table 14 of Viscarra Rossel et al. (2016) gives a comprehensive summary of data for SOC, mostly with statistics (R2, etc), and establishes that SOC of soils from different continents can be estimated by VNIR spectroscopy. The first results of the 5-year-old National Resources Conservation Service Rapid Carbon Assessment Project (NRCS-RaCA) have been published (Wijewardane et al., 2016a). RaCA has collected 144,000 + soil horizon VNIR spectra from across the coterminous United States. C and N contents were measured with dry combustion methods. SOC was taken to be the difference between the total SOC and measured inorganic C. The 19,891 sample spectra modeled consisted of 10 nm bands and 215 points. PCA rejected 87 outliers. The remaining samples came from 17 RaCA regions, 6 land-use-land-cover categories, 5 master horizons, and 10 soil texture classes as auxiliaries used to compartmentalize the data. 60%

Comparison of SOM in Organic and Conventional Soils

27

randomly chosen spectra from each region were used for training and 40% were composited. Each spectrum was processed with PLSR and Artificial Neural Network (ANN) models. Nonlinear outperformed linear models (Wijewardane et al., 2016b). PLSR included 1–30 latent factors (Lv) for PLSR, and ANN nLV had the lowest RMSECV (root mean squared error of cross validation). Model performances were assessed from R2, bias, RMSEp (root mean squared error of prediction), RPD, and RPIQ (ratio of performance to quartile range), suggested as a better index for nonnormal variables (Bellon-Maurel et al., 2010). Surface horizons accounted for 30% of the sample set. The predicted SOC and TC were satisfactory according to Fearn (2001). Chemical SOC measurements are uncertain, tedious, and expensive, so spectral SOC determinations are attractive. Spectral SOC data also relate stoichiometrically to atmospheric CO2 levels. What is measured is the sum of labile and sequestered C in a soil sample. To our knowledge, only Viscarra Rossel et al. (2016) and one other spectral study have differentiated between total and sequestered SOM. In a comparison with HS wet laboratory measurements, a hyperspectral line scanning camera has been used to image three horizons from surface to ca. 35 cm depth of an old, thick Tangelhumus organic profile located in south-east Germany in which the horizons are not readily visible to the eye (Steffens and Buddenbaum, 2013; Steffens et al., 2014). Spectra were recorded in the 410–990 nm range with 160 spectral bands. All spectra exhibited small reflectance in the visible range and increasing reflectance from 700 to 960 nm. The spectra were resolved into 20 subclasses of three groups in soil images A, B, and C at increasing soil depth. The three groups and their percentages in each layer were assigned from cross-polarization magic angle spinning nuclear magnetic resonance measurements and spectral slope data in terms of increasing extents of litter decomposition: (a) recognizable remains (living and dying roots with a small degree of decomposition), (b) decomposed POC from fragmented leaf tissue, and (c) decomposed amorphous SOM resulting from homogenization on humification (Steffens and Buddenbaum, 2013: see Fig. 6 for very good regression of measured vs estimated SOC with three different spectral processing approaches). To quote Steffens et al. (2014), “Different extents of decomposition could be separated based on the characteristic reflectance of the particles.” The potential of VNIR imaging for the classification of organic soil horizons as labile or sequestered is evident in this work (Steffens et al., 2014).

28

Elham A. Ghabbour et al.

The approach used in the research described in Section 6 differentiates between the labile and sequestered pools and serves as a measure of soil quality in the long term. Hopefully, soil spectroscopy as practiced by Steffens and coworkers is a viable alternative with the same purpose. Only time will tell.

7. CONCLUSIONS The information presented in this chapter highlights the importance of SOM components in soil health and highlights the potential of organic agriculture to increase the amount of carbon sequestered in the soil, hence contributing to climate change mitigation.

ACKNOWLEDGMENTS We thank the V K Rasmussen Foundation and the Farmers Advocating for Organics Fund for financial support of the NSP. We express our deepest gratitude to the hundreds of soil sample donors across the nation who made this work possible.

REFERENCES Abrahams, A.D., 1986. Hillslope Processes. Allen & Unwin, London. Aguilera, E., Lassaletta, L., Sanz-Cobena, A., Garnier, J., Vallejo, A., 2013. The potential of organic fertilizers and water management to reduce N2O emissions in Mediterranean climate cropping systems. A review. Agric. Ecosyst. Environ. 164, 32–52. Akramkhanov, A., Martius, C., Park, S.J., Hendrickx, J.M.H., 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma 163 (1), 55–62. Amundson, R., Berhe, A.A., Hopmans, J.W., Olson, C., Sztein, A.E., Sparks, D.L., 2015. Soil science. Soil and human security in the 21st century. Science 348, 648–653. Arachchi, M.H., Field, D.J., McBratney, A.B., 2016. Quantification of soil carbon from bulk soil samples to predict the aggregate-carbon fractions within using near-and mid-infrared spectroscopic techniques. Geoderma 267, 207–214. Arshad, M.A., Schnitzer, M., Angers, D.A., Ripmeester, J.A., 1990. Effects of till vs no-till on the quality of soil organic matter. Soil Biol. Biochem. 22 (5), 595–599. Beare, M.H., Hendrix, P.F., Coleman, D.C., 1994. Water-stable aggregates and organic matter fractions in conventional-and no-tillage soils. Soil Sci. Soc. Am. J. 58 (3), 777–786. Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.M., McBratney, A., 2010. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends Anal. Chem. 29 (9), 1073–1081. Blanco-Canqui, H., Lal, R., 2004. Mechanisms of carbon sequestration in soil aggregates. Crit. Rev. Plant Sci 23, 481–504. Blanco-Canqui, H., Lal, R., 2008. No-tillage and soil-profile carbon sequestration: an on-farm assessment. Soil Sci. Soc. Am. J. 72 (3), 693–701. Boardman, J., 2006. Soil erosion science: reflections on the limitations of current approaches. Catena 68 (2), 73–86. Bot, A., Benites, J., 2005. The importance of soil organic matter: key to drought-resistant soil and sustained food production. FAO Soils Bull. 94, 5–9.

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FURTHER READING Bouwman, L., Goldewijk, K.K., Van Der Hoek, K.W., Beusen, A.H., Van Vuuren, D.P., Willems, J., Rufino, M.C., Stehfest, E., 2013. Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period. Proc. Natl. Acad. Sci. U. S. A. 110 (52), 20882–20887.