Ellen diana van lutsenburg maas 2017 the cascading effects of climate change on soil organic matter

The Cascading Effects of Climate Change on Soil Organic Matter

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Ellen Diana van Lutsenburg Maas B.Sc. Graduate Program in Environmental Science

The Ohio State University 2017

Master's Examination Committee: Dr. Rattan Lal, advisor Dr. Berry Lyons Dr. Alvaro Montenegro

Copyright by Ellen Diana van Lutsenburg Maas 2017

Abstract Soil provides many ecosystem services, perhaps the most vital of which is the global regulation of greenhouse gases, such as CO2. Land use changes and management practices have contributed to the excess levels of CO2 in the atmosphere, as well as diminished soil’s capacity to sequester it. Many of these effects relate directly to the quantity of organic matter in soil (OM), which is critical to many of soil’s functions, including the infiltration and retention of water. Much is yet unknown about the effects of temperature and moisture on the rate of OM decomposition, but it is established that they increase the rate in most agricultural contexts. It has also been predicted that temperature and precipitation will both increase in the coming decades as a result of climate change in many locations globally. It follows that if OM content decreases, this will also have an effect on the availability of water to plants, resulting in increased drought conditions. This research investigated the likelihood of this chain-reaction occurring in northern Ohio. The two specific objectives were: (1) to project future levels of OM at two corn-based agricultural sites under different tillage treatments and climate change scenarios, and (2) to establish whether there was a direct relationship between OM and available water capacity in the Major Land Use Areas each site represents. Results indicate that OM content could decrease by 2070 under some situations, and that a weak correlation does exist between OM content and available water capacity in this region.

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Dedication I dedicate this thesis to my family who have been my biggest supporters as they all cheered me on from home in St. Louis, MO. To my parents, Clifford and Betty Smith: thank you for your unconditional love and support throughout my life, and for instilling a sense in me that I could accomplish anything I set my mind to. To my children, Deanna and Hayley Scheck: thank you for being my cheerleaders and unquestioningly supporting this significant and unusual step in my life. To my husband, Dirk: you are my rock. How can I thank you enough for loaning me out to OSU to pursue my dream while you kept our household running in my absence, for all the miles you so willingly drove between St. Louis and Columbus to visit, and after two years, still being willing to support my desire to take the next step and pursue a PhD here? My family’s love is the fuel that keeps me running.

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Acknowledgments Thank you, Dr. Lal, for accepting me so quickly into this program. Your patience and understanding have been vital to my success as I navigated the speed bumps of a career change, taking on an entirely new set of skills and knowledge, and doing so remotely from my family. I have been inspired your immense knowledge, strength of character, and calm direction.

Thank you, C-MASC and ESGP personnel, for your moral and practical support, particularly Laura Conover for your endless schedule coordination and both Laura and Maura Eze for your substantial and critical administrative help, Dr. Jose Guzman for readily sharing your expertise, Dr. Klaus Lorenz for keeping us all informed about the latest relevant soil research and for your refinement suggestions on my seminars, and Basant Ramal for your calm support and obvious love for all of us.

Thank you, fellow C-MASC students, for your friendship and support. We are all in this together! I am particularly grateful for Dr. Pat Bell and Reed Johnson’s help with my research and some homework, and especially so for Chris Eidson and Nall Moonilall’s willing and steady friendship, support, guidance, and suggestions.

Thank you, committee members. Dr. Alvaro Montenegro, your advice and encouragement have been strengthening to me, and your input into Chapter 2, which was accepted for publication, absolutely critical. Dr. Berry Lyons, your quiet strength and deep competence as a scientist has been a calming influence and much appreciated support. iv

Thank you, Dr. Kevin Coleman, for going way “above and beyond” in answering my endless questions and requests for support for over a year in working with the RothC carbon model. This paper, and probably my degree, simply wouldn’t have happened without your patient, tireless support.

Thank you, CSCAP and C-MASC, for providing the lion’s share of my funding. This research is part of a regional collaborative project supported by the USDA-NIFA, Award No. 2011-68002-30190 “Cropping Systems Coordinated Agricultural Project (CAP): Climate Change, Mitigation, and Adaptation in Corn-based Cropping System” sustainablecorn.org.

Thank you also to The Garden Club of America for financially supporting me through the summer via the Katherine M. Grosscup Scholarship.

Thank you, Dirk Maas, my very dear husband, for serving as a skilled and willing proofreader for this and other papers.

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Vita 1988…………………………………………Principia Upper School 1998…………………………………………B.S. Computer Science, Principia College 1998-2014……………………………………Application Developer, The Principia 2015-2016……………………………………Graduate Research Associate, Carbon Management and Sequestration Center, The Ohio State University 2016-present………………………………….Graduate Teaching Assistant, School of Environment and Natural Resources, The Ohio State University

Publications Maas, E.D.v.L., R. Lal, K. Coleman, A. Montenegro, W.A. Dick. (in press). Modeling Soil Organic Carbon in Corn-based Systems in Ohio Under Climate Change. Journal of Soil and Water Conservation.

Presentations Climatic Drivers of Soil Organic Matter Decomposition. ASA, CSSA, and SSSA Annual Meeting – Resilience Emerging from Scarcity to Abundance. November 6-9, 2016.

Fields of Study Major Field: Environmental Science

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Table of Contents

Abstract .................................................................................................................................................. ii Dedication ............................................................................................................................................ iii Acknowledgments ............................................................................................................................... iv Vita......................................................................................................................................................... vi Table of Contents ............................................................................................................................... vii List of Tables......................................................................................................................................... x List of Figures ...................................................................................................................................... xi Chapter 1: Introduction ....................................................................................................................... 1 1.1

The Soil/Climate Connection........................................................................................ 1

1.2

About Soil Organic Matter............................................................................................. 3

1.3

Environmental Factors on Organic Matter Decomposition..................................... 5

1.3.1

Temperature Effects ................................................................................................... 5

1.3.2

Moisture Effects .......................................................................................................... 7

1.3.3

Additional Effects .....................................................................................................12

1.4

Statement of Purpose....................................................................................................14 vii

1.4.1

Importance of the Current Study............................................................................15

1.4.2

Sustainable Corn CAP Overview ............................................................................16

1.4.3

Objectives ...................................................................................................................17

1.4.4

Hypotheses .................................................................................................................17

1.5

References.......................................................................................................................18

Chapter 2: Modeling Soil Organic Carbon in Corn-based Systems in Ohio Under Climate Change .................................................................................................................................................33 2.1

Abstract ...........................................................................................................................33

2.2

Introduction ...................................................................................................................34

2.3

Materials and Methods..................................................................................................36

2.3.1

Soil Carbon Model ....................................................................................................36

2.3.2

Historical Weather Data ...........................................................................................38

2.3.3

Regional Climate Model ...........................................................................................38

2.3.4

Agricultural Sites........................................................................................................40

2.3.5

Modeling Run Setup .................................................................................................46

2.4

Results and Discussion .................................................................................................50

2.4.1

Observed Values........................................................................................................51

2.4.2

Best-fit and Projection Runs....................................................................................54

2.5

Summary and Conclusions ...........................................................................................60

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2.6

Acknowledgements .......................................................................................................61

2.7

References.......................................................................................................................62

Chapter 3: Modeling Soil Organic Matter and Soil Moisture Retention ....................................77 5.1

Abstract ...........................................................................................................................77

5.2

Introduction ...................................................................................................................78

5.3

Methods ..........................................................................................................................83

5.4

Results and Discussion .................................................................................................88

5.5

Conclusion ......................................................................................................................99

5.6

Acknowledgements .....................................................................................................100

5.7

References.....................................................................................................................101

Chapter 4: Conclusion .....................................................................................................................108 Complete Reference List .................................................................................................................109

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List of Tables Table 1. Data collected for the Wooster site. .................................................................................42 Table 2. Data collected for the Hoytville site .................................................................................43 Table 3. Selected total organic carbon levels generated by the RothC model ...........................56 Table 4. Ohio counties included in the study .................................................................................82 Table 5. Results of individual predictors of available water content ..........................................86 Table 6. Coefficients (loadings) on principal components ...........................................................92 Table 7. Top three and last three results of multi-variate model testing ....................................93 Table 8. Soil and site parameters with their percent representation ...........................................97

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List of Figures Figure 1. A positive climate feedback loop....................................................................................... 2 Figure 2. Global projected temperature changes under two different climate change scenarios................................................................................................................................................. 5 Figure 3. Global predicted precipitation changes under two climate change scenarios............. 8 Figure 4. Progression of soil water effects on soil organic matter decomposition. .................... 9 Figure 5. Locations of field sites of the CSCAP project. ..............................................................16 Figure 6. Wooster bulk density and soil organic carbon contents over time. Dashed lines follow the full set of recorded data from experiment initiation, including initial measurements taken off-site. Solid lines only follow data actually collected from the site, excluding post-2006 samples at the plow-tillage site after field leveling. Round and square data points indicate bulk density and organic C, respectively. Starred (*) points represent bulk density values calculated from the bulk density trend line. .................................................45 Figure 7. Hoytville bulk density and soil organic carbon contents over time. Dashed lines follow the full set of recorded data from experiment initiation, including initial measurements taken off-site. Solid lines only follow data actually collected from the site.

Round and square data points indicate bulk density and organic C, respectively. Starred (*) points represent bulk density values calculated from the bulk density trend line.....................47 Figure 8. Observed total organic carbon (C) content (corrected to equivalent soil mass) for data actually collected from (a) the Wooster site (3,115 Mg ha–1 at approximately 25 cm depth) and (b) the Hoytville site (2,990 Mg ha–1 at approximately 23 cm depth), with trend lines (solid and dashed representing no-till and plow-till, respectively). Wooster plow-till data excludes post-2006 samples at the site after field leveling. ..........................................................52 Figure 9. Trends in mean annual temperature (MAT) and total annual precipitation (TP) over Wooster and Hoytville for weather data used to build the low-emissions (observed data) and high-emissions (modeled data) scenarios. Red lines (lower three in each graph) are annual mean temperature. Blue lines (upper three in each graph) are annual total precipitation. Solid lines are from observed (NOAA) values over the duration of the respective experimental periods. Dashed lines are modeled (CRCM historical reproduction) values from 1968-2000.

Dash-dot lines are modeled (CRCM future projection) values from 2038-2070. .....................53 Figure 10. Results of modeling runs for Wooster no-till and plow-till sites under low- and high-emissions scenarios (upper and lower lines in each graph, respectively) comparing modeled total organic carbon (TOC) to observed TOC, corrected to equivalent soil mass (ESM), which equates to approximately 25 cm (10 in) depth at this site. The dashed vertical line divides the graph into historical reconstruction (best-fit) and projection periods. ...........54 Figure 11. Results of modeling runs for Hoytville no-till and plow-till under low- and highemissions scenarios (upper and lower lines in each graph, respectively) comparing modeled xi

total organic carbon (TOC) to observed TOC, corrected to equivalent soil mass (ESM), which equates to approximately 23 cm (9 in) depth at this site. The dashed vertical line divides the graph into historical reconstruction (best-fit) and projection periods. ..................55 Figure 12. Correlation diagrams between observed (equivalent soil mass – see tables 1 and 2) and modeled total organic carbon (TOC) (see table 3) for Wooster and Hoytville, no-till and plow-till sites, for the best-fit model runs corresponding to the low-emissions scenarios through 2015. Wooster plow-till diagram only includes data through 2005. ............................57 Figure 13. Major land resource areas (MLRA) chosen for this study. Blue = MLRA 99, red = MLRA 111B, yellow = MLRA 139..................................................................................................83 Figure 14. Descriptive statistics for soil organic matter (OM) and available water capacity (AWC) after filtering Web Soil Survey data to prepare for modeling. .......................................84 Figure 15. Descriptive statistics for soil organic matter (OM), available water capacity (AWC), field capacity (FC), and permanent wilting point (PWP) before filtering for modeling, Web Soil Survey data. ......................................................................................................88 Figure 16. Descriptive statistics for soil organic matter (OM), available water capacity (AWC), field capacity (FC), and permanent wilting point (PWP) before filtering for modeling, National Cooperative Soil Survey data. ........................................................................89 Figure 17. Percent of variance explained by each factor in the Principle Component Analysis (PCA) for the NCSS and WSS data sets. White numbers in parenthesis are the eigenvalues for each factor.....................................................................................................................................91 Figure 18. Scores from the first two principal components of the a) NCSS and b) WSS data sets plotted together with the relative influences of each soil attribute on the two factors.

(Due to a software bug in Matlab, the clay line is obscured in b). ..............................................92 Figure 19. Modeling soil organic matter (OM) to available water capacity (AWC) from the Web Soil Survey data with a linear (black line) and polynomial (blue line) models. ................94 Figure 20. Modeling soil organic matter (OM) to available water capacity (AWC) from the National Cooperative Soil Survey data with a linear (black line) and polynomial (blue line) models. .................................................................................................................................................95 Figure 21. Descriptive statistics for soil organic matter (OM) and available water capacity (AWC) with adjusted filtering Web Soil Survey data to prepare for modeling. ........................96 Figure 22. Modeling organic matter (OM) to both field capacity (FC) and permanent wilting point (PWP), Web Soil Survey data. Upper half is FC, lower half is PWP. Black lines are linear models, blue lines are polynomial, and dashed red are theoretical logarithmic relationships, added for comparison. ..............................................................................................98

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Chapter 1: Introduction 1.1

The Soil/Climate Connection Soil provides many ecosystem services, including a vital role in the global regulation

of carbon dioxide (CO2) and other greenhouse gases (GHG) (Hatfield et al. 2014). Between 2005 and 2014, the “land sink,” including soil and vegetation, sequestered 30% of CO2 emissions (160±60 Gt C [176±66 billion tn]), including fossil fuel burning and land use change (Le Quéré et al. 2015). Within the global land sink, soil to a 1 m (3.3 ft) depth contains two to three times the amount of C as the atmosphere (Lal 2004, Lal 2010).

Agricultural practices and land use change have caused the loss of an estimated 30 to 40 Mg C ha-1 (13.4 to 17.8 tn C ac-1) in croplands and 40 to 60 Mg C ha-1 (17.8 to 26.8 tn C ac-1) in degraded soils since 1850 (Lal 2004). Natural land converted to agricultural use can lose up to 50% of its C within 50 years in temperate-zone climates (Lal et al. 2011). Tillage practices that break up and invert the soil affect decomposition in multiple ways, such as breaking apart aggregates containing physically-protected soil organic carbon (SOC) and exposing more surface area to heat, moisture, and oxygen (Uri 2001). This has resulted in increased GHG levels in the atmosphere and the loss of soil organic matter (OM) and, consequently, SOC. Climate change can affect the global hydrological cycle in a positive-feedback loop (Figure 1). Rising temperatures result in increased potential evapotranspiration, which result in drier soils, which in turn cause increases in local and regional temperatures (Feddema

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1998), and increased risk of extreme hot days and heat waves (IPCC 2014). While climate models are predicting changes in factors that affect soil, such as temperature and precipitation (IPCC 2014), soil

Figure 1. A positive climate feedback loop.

moisture content in turn can

Rising Temperatures

substantially affect local circulation and precipitation patterns (Feddema 1998). Soil’s water holding capacity is

Drying Soils

Increased Potential Evapotranspiration

controlled by its retention pores, as determined by aggregate and pore size distribution, both strongly moderated by SOC concentration and its dynamics (Blanco-Canqui and Lal 2004, Zhuang et al. 2008). When soil water-holding capacity is decreased, moisture surpluses are attained with less rainfall, resulting in increased runoff and increased moisture deficit during dry seasons (Feddema 1998). Increased moisture deficit during dry periods will result in longer and more intense dry periods (Feddema and Freire 2001).

It is essential to understand the nature of OM and the factors influencing its decomposition to effectively predict future SOC stocks and soil water content, which will be critical for farmers and other land owners to make informed decisions regarding land management practices.

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1.2

About Soil Organic Matter Soil OM is a key soil health indicator (Reeves 1997). It sustains key soil functions

that promote soil aggregation, which also supports water infiltration, by supplying the energy and substrates needed to support biological diversity and activity (Franzluebbers 2002).

OM decomposition is dependent upon environmental factors, substrate availability, chemical composition, microbial community size and composition, and soil-plant interactions (Conant et al. 2011, Dungait et al. 2012, Erhagen et al. 2013, Creamer et al.

2015, Hill et al. 2015, Pang et al. 2015).

Conant et al. (2011) compiled a conceptual model of decomposition to help facilitate scientific inquiry and exploration of the decomposition process, particularly when investigating climatic influences on its rate. They grouped processes into “protected OM” and “unprotected OM”. Protected OM processes relate to aggregation and substrate availability and include adsorption/desorption, diffusion/dissolution, and aggregate turnover. As protected OM becomes available for decomposition, it undergoes depolymerization via enzymes, microbial uptake and catabolism, which results in further enzyme production. Resistance to decomposition is due to either chemical conformation (related to depolymerization) or physio-chemical protection (related to adsorption/desorption and aggregate turnover) (Conant et al. 2011). However, questions regarding whether chemical composition or microbial accessibility to substrate is more important to long-term SOC stabilization, remain (Dungait et al. 2012, Erhagen et al. 2013, Creamer et al. 2015, Moinet et al. 2016).

Decomposed elements of flora and fauna furnish the soil with OM for aggregate structure development which, in turn, provides the framework for water, air, and nutrient

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flow to plants (Blanco-Canqui and Lal 2004). Aggregates are structured assemblages of organic substances and mineral particles of varying sizes and life-spans. The SOC in OM provides a critical interaction with clay to form organo-mineral complexes that create aggregates (Malamoud et al. 2009). Plant roots, fungal hyphae, mycorrhizal hyphae, bacterial cells, and algae – “temporary agents” (Tisdale and Oades 1982) – provide the original organic framing to accumulate various mineral soil particles together into macroaggregates (Blanco-Canqui and Lal 2004). As they begin to break down, polysaccharides from organic mucilages are released (Tisdale and Oades 1982, Greenland 1965) – “transient agents” (Tisdale and Oades 1982), or “labile SOC”. They are the most important component of humus for aggregation (Chenu and Guerif 1991), although they are easily decomposed and therefore their effect on stabilizing aggregates only lasts a few weeks (Wild 1988). Clay particles are adsorbed to the surfaces of these substances, further forming and stabilizing the macroaggregates (Blanco-Canqui and Lal 2004). Humic compounds, polymers, and polyvalent cations are highly decomposed materials – “persistent agents” (Tisdale and Oades 1982), or “recalcitrant SOC” – and associated with microaggregation and long-term SOC sequestration (Blanco-Canqui and Lal 2004). They form organo-mineral complexes with adsorbed clay particles in the inner regions of microaggregates, and have a long-lasting effect on microaggregate dynamics (Edwards and Bremner 1967).

Blanco-Canqui and Lal (2004) noted a beneficial partnership between OM and aggregation: OM promotes soil aggregation, whereas aggregates in return store OM, reducing its rate of decomposition.

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1.3

Environmental Factors on Organic Matter Decomposition Many environmental factors can influence the rate of OM decomposition,

particularly those related to climate change, such as temperature, precipitation, and elevated atmospheric CO2 concentrations.

1.3.1

Temperature Effects Figure 2 depicts the global projected temperature changes – almost entirely increases

of varying scope – under two different IPCC climate change scenarios (IPCC 2014).

Scenario RCP 2.6 assumes rapid reductions in

Figure 2. Global projected temperature changes under two different climate change scenarios.

RCP2.6

RCP8.5

emissions – more than 70% cuts from current levels by 2050 – resulting in a much Source: IPCC (2014)

smaller degree of

warming than scenario RCP 8.5, which assumes continued increases in emissions, attended by associated large increases in warming (IPCC 2014).

What effect does temperature have on OM decomposition? It can be helpful to answer this question through the components of kinetic theory (Conant et al. 2011).

The first component of kinetic theory as applied to soil OM decomposition states that the rate of decomposition increases with an increase in temperature. This is straightforward and generally accepted (Conant et al. 2011).

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The second component of kinetic theory as related to decomposition states that the rate of change of decomposition is maximum at lower temperatures. This is also basically accepted, although there is some disagreement regarding the rate of change itself (Kirschbaum 1995, Kirschbaum 2006, Agren and Wetterstedt 2007, Conant et al. 2011).

The general temperature response equation of biological systems, the “Q 10â€? function, is the factor by which the rate of a reaction increases for every 10oC rise in temperature (Equation 1). Typically, this value is a constant between 2-3 for biological systems. đ?‘„10 = (đ?‘˜2 â „đ?‘˜1 )[10â „(đ?‘‡2−đ?‘‡1)]

(1)

The variables k1 and k2 are the rate constants recorded for a particular process, such as respiration, at two observed temperatures T1 and T2.

In soil, however, there have been decreasing rates observed as temperature increases (Agren and Wetterstedt 2007). In fact, over the years there have been a wide variety of models produced to try to explain the observed changes in rates, summarized by Kirschbaum (2006). Laboratory incubations tended to see Q 10 values as high as 8 at low (near 0oC) temperatures, while field studies tended to be lower, around Q10 of 4. Both laboratory and field measured Q10 at close to 2 at higher (30oC) temperatures (Kirschbaum 2006). Davidson et al (2006) speculated that Q10 values >2.5 were related to substrate supply or other factors. Microbial efficiency is also known to decrease with increasing temperatures (Frey et al. 2013).

The third component of kinetic theory as applied to soil expects a greater rate of change of slow-pool (recalcitrant) OM decomposition than fast-pool (labile) OM with rising temperatures (Conant et al. 2011). This implies that decomposition reactions with high activation energies (i.e., slow rates) will experience greater proportional increases with 6

increasing temperature than will those with low activation energy (i.e., fast rates). Its applicability to soil OM decomposition remains uncertain, and this is where there is the most debate (Conant et al. 2011). There is evidence that new (labile) and old (recalcitrant) soil carbon responds equally to increasing temperature (Conen et al. 2006).

The key question to resolve is how temperature affects the individual processes that control decomposition. The temperature response (Q10) of the bulk soil is a collective response of a variety of processes involved in decomposition, each with its own temperature sensitivity, including: 

the rate at which substrate supplies C to the soil solution,



the diffusion rate that delivers the C to the microbes, and



the rate at which the microbes assimilate the C (Agren and Wetterstedt 2007).

One or more factors can be limiting to the others given the parameters of the system, such as the chemical composition of the substrate, the amount of water in the soil and related potential energies, and the types of microbes in the system (Agren and Wetterstedt 2007).

When any of these variables changes, the overall Q10 for the system changes.

1.3.2

Moisture Effects Figure 3 depicts the global projected precipitation changes under two different IPCC

climate change scenarios (IPCC 2014). In both scenarios, predictions indicate a variety of fluctuations in precipitation. In the United States, projections are for increases in precipitation over many areas key to agriculture (Melillo et al. 2014). There are also projections for wide variations between seasons and increases in very heavy precipitation events (Melillo et al. 2014). Despite this, soil moisture is projected to decrease with

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increasing temperature and the lengthening of dry spells between

Figure 3. Global predicted precipitation changes under two climate change scenarios.

RCP2.6

RCP8.5

precipitation events (Melillo et al. 2014).

Increases in average precipitation and

Source: IPCC (2014)

intensity of downpours will also increase the likelihood of water erosion. Erosion removes the upper layers of soil first, which usually contain the highest concentrations of OM, and therefore SOC, in the soil profile (Kaurin et al. 2015).

Soil water (SW) has a direct effect on the OM content. This relationship is widely accepted although some of the technical mechanisms remain little understood (Moyano et al.

2013). Soil OM provides a foundational food source for a host of decomposer organisms including bacteria, fungi, and soil fauna, so the primary process by which OM is decomposed is through microbial processing (Figure 4). Therefore, the effect of soil water on total SOC content is most commonly measured through soil respiration (Paul et al. 2003).

However, there are two types of soil respiration: autotrophic and heterotrophic. Autotrophic respiration relates to the “breathing� of plant roots and associated microbes, which have no appreciable effect on OM decomposition processes (Kuzyakov 2006). Heterotrophic respiration (RH) relates to the microbial pool responsible for decomposition.

Microbial activity, and therefore RH, is highest where an optimal balance of SW and oxygen availability exists. When soil dries, the SW is progressively disconnected as the 8

remaining water is retained in smaller and smaller pores, which limits the flow of substrates to microbes (Schjonning 2003) and thus limits their activity. When soil approaches saturation, microbial activity also drops due to reduced access to O2, the diffusion rate of Figure 4. Progression of soil water effects on soil organic matter decomposition.

which is 104 times smaller in water than in air (Schjonning et al.

2003). This relationship of SW to RH can, therefore, be expressed with a simple parabolic bell curve (Moyano et al.

2013). The shape and scale of this curve is affected by several physical, chemical, and historical factors, including various soil properties, OM content, current O2 and substrate availability, and historical patterns of rainfall, soil moisture, and microbial activity. Total porosity is related to bulk density and affects the diffusion of gases, as increased pore space at equivalent water content increases oxygen availability and thus respiration (Moyano et al. 2013). Respiration decreases with increasing clay content (Franzluebbers 1999) and decreases as total substrate

Source: Adapted from Moyano et al. (2013)

supply decreases at a given water content (Moyano et al. 2013).

OM content alters many properties, such as water retention and

pore space, that can affect diffusion of O2 and substrate, and has effects similar to clay due to its high specific surface area (Rawls et al. 2003, Moyano et al. 2013).

The SW-RH relationship is also affected by temporal factors. “Current� O 2 diffusivity is hindered and substrate availability is facilitated by increasing water content (Manzoni et al.

2012). However, Averill et al. (2016) found that historical climate could account for 50% of the variation in maximum microbial activity, independent of temperature, soil texture and 9

SOC variables. In locations where soils are historically drier, the production of enzymes becomes increasingly sensitive to SW, and limited by N availability (Averill et al. 2016).

The SW-RH curve displays different shapes depending upon the moisture regime.

Soils at a relatively constant moisture content, such as those under laboratory conditions, eventually reach an equilibrium in microbial activity, and substrate and gas concentrations (Moyano et al. 2013). Moisture levels under normal field conditions undergo regular fluxes in SW content, which result in transient changes to the SW-RH relationship known as the Birch effect (Birch 1958). The Birch effect observes that the sequence of drying and rewetting results in increased C and N mineralization over constantly moist soils due to multiple possible causes including death of microbial biomass (Kieft et al. 1987), release of intracellular osmolytes accumulated during the dry period (Schimel et al. 2007), rupture of soil aggregates due to weakening of bond strength with water increase (Kay 1998, Miller et al.

2005), and enzymatic activity that can continue at lower water potentials than microbial activity (Lawrence et al. 2009). This effect is countered somewhat by an increase in soil water repellency after drying-wetting cycles, especially with increased temperature, which reduces the availability of substrates (Goebel 2011).

The shape and interpretation of the SW-RH curve are also affected by the various units used to express soil moisture, such as gravimetric, volumetric, relative water saturation, relative water holding capacity and matric potential (Moyano et al. 2013). Water matric potential was found to have the most “stable” relationship – meaning, the least affected by soil characteristics – with respiration, with relative microbial activity showing a negative, near-linear correlation with the log10 of water potential (Moyano et al. 2013).

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Despite these advances in understanding the relationship between SW and R H, correlation may not be causation. Moinet et al. (2016) used nondisruptive in situ field techniques to measure total soil respiration and concluded that soil temperature and water content were strong drivers of changes in autotrophic respiration, but not RH. They attributed changes in RH to soil physical properties associated with physical protection of organic matter, especially particulate organic matter (physically unprotected labile carbon) and soil specific surface area (Moinet et al. 2016).

1.3.2.1

Reciprocal Effect of Organic Matter on Moisture Soil OM, in turn, has a direct effect on SW content, by influencing the physical

properties that determine a soil’s capacity to infiltrate and retain water. For example, the binding agents OM contributes to soil provide a slight water-repellency when binding aggregates, which influences water flow (Wang 2009). This aids stability by reducing the build-up of air pressure inside the aggregates which can rupture them (Blanco-Canqui, 2011).

This hydrophobic property is enhanced when SOC associates with the finest clay fractions (Spaccini 2002).

Because the mineral soil component is about five times more dense (1.55/0.3) than OM (Hudson 1994), its incorporation into soil effectively lowers the bulk density through “dilution”, which can increase hydraulic conductivity (Blanco-Canqui and Benjamin 2013).

Soil compaction may be the most affected by changes in SOC content than other soil physical properties (Blanco-Canqui and Benjamin 2013). Soil OM increases total porosity, macroporosity, and pore continuity in a relationship that can be either linear or exponential due to its aggregate architecture, as well as its ability to stimulate soil organisms such as earthworms to create water-conducting biopores (Blanco-Canqui and Benjamin 2013). SOC 11

has stronger effect than clay on pore volume, water retention, and soil structure (Boivin 2009), specific surface area, and water adsorption capacity (Rawls 2003). Without OM, water retention is a function of pore space and mineral surface area. As OM is added, it is adsorbed onto the mineral surfaces. As OM content increases, the added particles will either adsorb to a mineral surface, or to prior OM added and already adsorbed to a mineral surface.

Eventually, most additions of new OM must bond to pre-existing OM, creating an organic substrate with a mineralogical skeleton. Over time, the soil can change from a mineraldominated system to a carbon-dominated system (Olness and Archer 2005). The result is a variable contribution of OM and mineral surfaces to the total water holding capacity.

Chapter 3 describes effect of OM on SW content in deeper detail.

1.3.3

Additional Effects Elevated CO2 (eCO2) concentration in the atmosphere is an additional factor which

is not accounted for in this study, due to the uncertainty of the effects at present. There is substantial evidence that eCO2 results in increased yield and both above- and below-ground biomass in most crops (Dijkstra and Morgan 2012), although this effect appears to vary in maize, showing increases in studies in China and India (Xie et al. 2015, Abebe et al. 2016), and no response in maize in Illinois unless under drought stress (Leakey et al. 2006, Dijkstra and Morgan 2012, Attavanich and McCarl 2014). Another study found increased biomass response in maize under drought stress but only where there was a sufficient supply of N (Zong and Shangguan 2014). Several recent studies report a decrease in SOC due to a higher rate of decomposition as a response to eCO2 (Moran and Jastrow 2010, Cheng et al. 2012, Pereira et al. 2013, van Groenigen et al. 2014, Keiluweit et al. 2015). Explanations include increased labile substrate availability (the priming effect, see below) and changes in microbial 12

composition leading to increased microbial utilization of soil C (Carney et al. 2007). Despite increases in C inputs due to stimulated plant growth at eCO2, both plant-based and microbial responses may more than counter it, resulting in overall loses of SOC (van Groenigen et al.

2014, Keiluweit et al. 2015). It has been also reported that additions of N over natural rates of atmospheric inputs are required to affect carbon sequestration under eCO2 (van Groenigen et al. 2006), and this is common in corn-based agricultural systems in the United States. Finally, recent studies (Hopkins et al. 2014, Osanai et al. 2015) have found the combined effects of eCO2 and increased temperature on the rate of decomposition to be greater than under either effect alone. In summary, eCO2 effects were not factored into the results, but increased growth could impact how C is processed and stored in soil. Earth system models currently do not account for the increased microbial response to additional C inputs due to eCO2 (Georgiou et al. 2015).

Microbial processes in general are excluded from this study. They are not directly included in the RothC model (see Chapter 2), though earth system models, in evaluating and predicting SOC, may perform with greater accuracy with the inclusion of microbial processing (Wieder et al. 2013, Cheng et al. 2014, van Groenigen et al. 2014). One key microbial process that influences the dynamics of OM decomposition and resulting SOC concentrations is called the “priming effect�. The priming effect is a process whereby microbial action is catalyzed by an influx of labile substrate (that which is easily assimilated by microbes), resulting in not only the mineralization of the introduced substrate, but also of pre-existing, more recalcitrant soil C (that which is less available to microbes). The rate and dynamics of this process are influenced by multitudinous factors, such as climate (including temperature, moisture, and atmospheric CO2 concentrations (Osani et al. 2015, Moyano et 13

al. 2013, Hopkins et al. 2014, van Groenigen et al. 2014)), soil physical properties (Sulman et al. 2014, Cui and Holden 2015), microbial composition (Carney et al. 2007), nutrient availability (van Groenigen et al. 2006), substrate quality (Wutzler and Reichstein 2013), rootsoil interactions (Keiluweit et al. 2015, Hill et al. 2015, Min et al. 2015), and even plant species (Zhu et al. 2014). There is ongoing debate regarding the drivers, scope, and direction of the process (Kuzyakov et al. 2000, Cheng et al. 2013).

One possible result of eCO2 and the priming effect is the accumulation of SOC (Cheng et al. 2014), another is soil becoming a C source in the future, rather than a sink (Carney et al. 2007), and a third is that the variable factors may balance each other, resulting in no net influence on SOC stocks (Reinsch et al. 2013).

1.4

Statement of Purpose Over the past two decades, it has become increasingly clear that climate change will

increase both temperature and precipitation by the end of the century in many regions that are currently key to agriculture (IPCC 2013, Melillo et al. 2014). On a global scale, the likelihood of continuing temperature increases is “virtually certain” (greater than 99% probability), and of heavy precipitation events is “very likely” by the end of the century, at least over mid-latitude land masses and already-wet tropical regions (IPCC 2013). These predictions apply to the Midwest region of the United States (Walsh et al. 2014, Hatfield et al. 2014), one of the most important agricultural areas in the world (Hatfield et al. 2012), where the combined stresses will have adverse effects on agriculture and are expected to decrease productivity (Janetos et al. 2008, Pryor et al. 2014).

Temperature and moisture are known to facilitate acceleration of decomposition rates, and loss of OM is one important process that degrades soil (Hatfield at al. 2014). All of

14

the aforementioned factors affect the capacity of soil to sequester and hold SOC that, in turn, affect the sustainability of agriculture and humanity’s ability to feed itself.

1.4.1

Importance of the Current Study Gaining a solid understanding of the relationship between climatic factors and OM

decomposition will be critical to forecasting future SOC stocks, as well as identifying potential side-effects. Rainfed agriculture is the dominant production system worldwide, but land area under irrigation has doubled since 1961 (FAO and ITPS 2015). Low productivity on rainfed cropland is blamed on low inherent soil fertility, severe nutrient depletion, poor soil structure and inappropriate soil management practices (FAO and ITPS 2015). The effects of dwindling OM and SW content are suspect as the root cause. Public policy would be an invaluable support in addressing these issues. However, considering that the timeframe of change of many soil physical properties exceeds the typical policy review cycle (1 to 5 years in the United States), it is not always possible to monitor changes that occur because of policy applications (Kibblewhite et al. 2016). Monitoring is also expensive and time-consuming (Makipaa et al. 2008, Jimenez and Ladha 1993). Modeling efforts are thus being evaluated as a cost-effective way to help policy makers make informed decisions about land use and management in ways that will support climate change mitigation efforts, and modeling is a key element to those efforts (Paustian et al. 2016). VĂśrĂśsmarty et al. (2000) warned that a deteriorating network of hydrometric monitoring will hamper future projections of water resource vulnerabilities. It is therefore critical that computer modeling continue to be developed as a tool to support mitigation efforts.

15

1.4.2

Sustainable Corn CAP Overview The Cropping Systems Coordinated Agricultural Project (CSCAP) was a 5-year

project funded by the USDA-NIFA, and provided much of the funding and data for this study. Data were collected from 35 field sites and thousands of farmers over 9 states in the Midwest (Figure 5) and included the participation of 10 US universities (CSCAP 2017). The primary goal of the project was the identification of farming practices, particularly in cornbased systems, that would result in retained and enhanced organic matter, reduced nitrogen Figure 5. Locations of field sites of the CSCAP project.

Source: CSCAP (2017)

loss, and increased resilience to extreme weather events. The project included 6 objectives, ranging in purpose from field research to public outreach. This study falls under Objective 3: Systems Analysis and Predictive Modeling (CSCAP 2017). Focus of this research is in northern Ohio, at and around the Hoytville, OH and Wooster, OH research sites.

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1.4.3

Objectives The objectives of this study are to:



Understand the nature of OM and the factors influencing the rates of its decomposition.



Project the fate of OM content in some agricultural soils in Ohio under different scenarios of climate change.



Characterize a potential side-effect of OM fluctuations in the water content of some Ohio soils.

1.4.4

Hypotheses This study’s hypotheses are:



From 2015 through 2070, levels of SOC in soils under no-till (NT) management in Ohio will show increasing trends under a low-emissions (LE) climate change scenario, decreasing trends under plow-till (PT) management in the LE scenario, and trends of depletion in both treatments under a high-emissions (HE) scenario.



Also, by 2070, levels of SOC in soils of both NT- and PT-managed sites in Ohio will be projected to decrease more under a HE than a LE climate change scenario.



OM is the best individual predictor of AWC.



A multi-variate model is more descriptive of the OM-AWC relationship than a onevariable model.

17

1.5

References

Abebe, A., H. Pathak, S.D. Singh, A. Bhatia, R.C. Harit, and V. Kumar. 2016. Growth, Yield and Quality of Maize with Elevated Atmospheric Carbon Dioxide and Temperature in North–west India. Agriculture, Ecosystems & Environment 218 (February 15, 2016): 66–72. Ågren, Göran I., and J. Å. Martin Wetterstedt. 2007. “What Determines the Temperature Response of Soil Organic Matter Decomposition?” Soil Biology and Biochemistry 39, no. 7 (July 2007): 1794–98. doi:10.1016/j.soilbio.2007.02.007.

Attavanich, W., and B.A. McCarl. 2014. How Is CO2 Affecting Yields and Technological Progress? A Statistical Analysis. Climatic Change 124, no. 4 (June 2014): 747–62.

Averill, Colin, Bonnie G. Waring, and Christine V. Hawkes. 2016. “Historical Precipitation Predictably Alters the Shape and Magnitude of Microbial Functional Response to Soil Moisture.” Global Change Biology 22, no. 5 (May 1, 2016): 1957–64. doi:10.1111/gcb.13219.

Birch, H. F. 1958. “The Effect of Soil Drying on Humus Decomposition and Nitrogen Availability.” Plant and Soil 10, no. 1: 9–31. doi:10.1007/BF01343734.

Blanco-Canqui, H., and R. Lal. 2004. “Mechanisms of Carbon Sequestration in Soil Aggregates.” Critical Reviews in Plant Sciences 23, no. 6 (2004): 481–504. doi:10.1080/07352680490886842.

Blanco-Canqui, H. 2011. Does no-till farming induce water repellency to soils? Soil Use Manage. 27:2–9. doi:10.1111/j.1475-2743.2010.00318.x.

Blanco-Canqui, Humberto, and Joseph G. Benjamin. 2013. “Impacts of Soil Organic Carbon on Soil Physical Behavior.” In Advances in Agricultural Systems Modeling, edited by

18

Sally Logsdon, Markus Berli, and Rainer Horn. Soil Science Society of America, Inc., 2013. https://dl.sciencesocieties.org/publications/books/abstracts/advancesinagric/quantify ingandm/11.

Boivin, P., B. Schäffer, and W. Sturny. 2009. Quantifying the relationship between soil organic carbon and soil physical properties using shrinkage modelling. Eur. J. Soil Sci. 60:265–275. doi:10.1111/j.1365-2389.2008.01107.x.

Carney, K.M., B.A. Hungate, B.G. Drake, and J.P. Megonigal. 2007. Altered Soil Microbial Community at Elevated CO2 Leads to Loss of Soil Carbon. Proceedings of the National Academy of Sciences of the United States of America 104, no. 12 (March 20, 2007): 4990–95.

Cheng, L., F.L. Booker, C. Tu, K.O. Burkey, L. Zhou, H.D. Shew, T.W. Rufty, and S. Hu. 2012. Arbuscular Mycorrhizal Fungi Increase Organic Carbon Decomposition Under Elevated CO2. Science 337 (August 31, 2012): 1084–87.

Cheng, Weixin, William J. Parton, Miquel A. Gonzalez-Meler, Richard Phillips, Shinichi Asao, Gordon G. McNickle, Edward Brzostek, and Julie D. Jastrow. “Synthesis and Modeling Perspectives of Rhizosphere Priming.” New Phytologist 201, no. 1 (January 2014): 31–44. doi:10.1111/nph.12440.

Chenu, C. and Guerif, J. 1991. Mechanical strength of clay minerals as influenced by an adsorbed polysaccharide. Soil Sci. Soc. Am. J. 55: 1076–1080.

Conant, R.T., M.G. Ryan, G.I. Ågren, H.E. Birge, E.A. Davidson, P.E. Eliasson, S.E. Evans, S.D. Frey, C.P. Giardina, F.M. Hopkins, R. Hyvonen, M.U.F. Kirschbaum, J.M. Lavallee, J. Liefeld, W.J. Parton, J.M. Steinweg, M.D. Wallenstain, J.A.M. Wetterstedt, 19

and M.A. Bradford. 2011. Temperature and Soil Organic Matter Decomposition Rates Synthesis of Current Knowledge and a Way Forward. Global Change Biology 17, no.

11 (November 2011): 3392–3404.

Conen, F., J. Leifeld, B. Seth, and C. Alewell. 2006. “Warming Mineralises Young and Old Soil Carbon Equally.” Biogeosciences 3, no. 4 (2006): 515–19.

Creamer, Courtney A., Lynne M. Macdonald, Jeff A. Baldock, Jonathan Sanderman, and Mark Farrell. “Divergent Responses of Organic Matter Composition to Incubation Temperature.” Geoderma 259–260 (December 2015): 279–87. doi:10.1016/j.geoderma.2015.06.019.

CSCAP. 2017. About This Project. Sustainable Corn.Org. Accessed March 1, 2017. https://sustainablecorn.org/About-Project/Index.html.

Cui, Junfang, and Nicholas M. Holden. “The Relationship between Soil Microbial Activity and Microbial Biomass, Soil Structure and Grassland Management.” Soil and Tillage Research, Soil Structure and its Functions in Ecosystems: Phase matter & Scale matter, 146, Part A (March 2015): 32–38. doi:10.1016/j.still.2014.07.005.

Davidson, E. A., I. A. Janssens, and Y. Q. Luo. 2006. “On the Variability of Respiration in Terrestrial Ecosystems: Moving beyond Q(10).” Global Change Biology 12, no. 2 (February 2006): 154–64. doi:10.1111/j.1365-2486.2005.01065.x.

Dijkstra, F.A., and J.A. Morgan. 2012. Elevated CO2 and Warming Effects on Soil Carbon Sequestration and Greenhouse Gas Exchange in Agroecosystems. In Managing Agricultural Greenhouse Gases, 467–86. Elsevier, 2012. http://linkinghub.elsevier.com/retrieve/pii/B9780123868978000279.

20

Dungait, Jennifer A. J., David W. Hopkins, Andrew S. Gregory, and Andrew P. Whitmore.

2012. “Soil Organic Matter Turnover Is Governed by Accessibility Not Recalcitrance.”

Global Change Biology 18, no. 6 (June 1, 2012): 1781–96. doi:10.1111/j.13652486.2012.02665.x.

Erhagen, Björn, Mats Öquist, Tobias Sparrman, Mahsa Haei, Ulrik Ilstedt, Mattias Hedenström, Jürgen Schleucher, and Mats B. Nilsson. “Temperature Response of Litter and Soil Organic Matter Decomposition Is Determined by Chemical Composition of Organic Material.” Global Change Biology 19, no. 12 (December 2013): 3858–71. doi:10.1111/gcb.12342.

Edwards, A. P. and Bremner, J. M. 1967. Microaggregates in soils. J. Soil Sci. 18: 65–73.

FAO and ITPS. 2015. Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy.

Feddema, Johannes. 1998. “Estimated Impacts of Soil Degradation on the African Water Balance and Climate.” Climate Reserach 10 (1998): 127–41.

Feddema, J. J., and S. Freire. “Soil Degradation, Global Warming and Climate Impacts.”

Climate Research 17, no. 2 (August 15, 2001): 209–16. doi:10.3354/cr017209.

Franzluebbers, A. J. 1999. “Microbial Activity in Response to Water-Filled Pore Space of Variably Eroded Southern Piedmont Soils.” Applied Soil Ecology 11, no. 1 (January 3, 1999): 91–101. doi:10.1016/S0929-1393(98)00128-0.

Franzluebbers, A. J. 2002. “Water Infiltration and Soil Structure Related to Organic Matter and Its Stratification with Depth.” Soil & Tillage Research 66, no. 2 (July 2002): 197– 205. doi:10.1016/S0167-1987(02)00027-2.

21

Frey, Serita D., Juhwan Lee, Jerry M. Melillo, and Johan Six. “The Temperature Response of Soil Microbial Efficiency and Its Feedback to Climate.” Nature Climate Change 3, no. 4 (April 2013): 395–98. doi:10.1038/nclimate1796.

Georgiou, Katerina, Charles D. Koven, William J. Riley, and Margaret S. Torn. “Toward Improved Model Structures for Analyzing Priming: Potential Pitfalls of Using Bulk Turnover Time.” Global Change Biology 21, no. 12 (December 2015): 4298–4302. doi:10.1111/gcb.13039.

Goebel, Marc-O., Jörg Bachmann, Markus Reichstein, Ivan A. Janssens, and Georg Guggenberger. 2011. “Soil Water Repellency and Its Implications for Organic Matter Decomposition - Is There a Link to Extreme Climatic Events?” Global Change Biology 17, no. 8 (August 2011): 2640–56. doi:10.1111/j.1365-2486.2011.02414.x.

Greenland, D. J. 1965. Interactions between clays and organic compounds in soils. Part II. Adsorption of soil organic compounds and its effect on soil properties. Soils and Fert.

28: 521–527.

Hatfield, J. 2012. Agriculture in the Midwest. In U.S. National Climate Assessment Midwest Technical Input Report. J. Winkler, J. Andresen, J. Hatfield, D. Bidwell, and D. Brown, coordinators. Available from the Great Lakes Integrated Sciences and Assessments (GLISA) Center, http://glisa.msu.edu/docs/NCA/MTIT_Agriculture.pdf Hatfield, J., G. Takle, R. Grotjahn, P. Holden, R.C. Izaurralde, T. Mader, E. Marshall, and D. Liverman. 2014. Ch. 6: Agriculture. In Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 150-174.

22

Hill, Paul W., Mark H. Garnett, John Farrar, Zafar Iqbal, Muhammad Khalid, Nawaf Soleman, and Davey L. Jones. “Living Roots Magnify the Response of Soil Organic Carbon Decomposition to Temperature in Temperate Grassland.” Global Change Biology 21, no. 3 (March 2015): 1368–75. doi:10.1111/gcb.12784.

Hopkins, F.M., T.R. Filley, G. Gleixner, M. Lange, S.M. Top, and S.E. Trumbore. 2014.

Increased Belowground Carbon Inputs and Warming Promote Loss of Soil Organic Carbon through Complementary Microbial Responses. Soil Biology & Biochemistry 76 (September 2014): 57–69.

Hudson, Berman D. 1994. “Soil Organic Matter and Available Water Capacity.” Journal of Soil and Water Conservation 49, no. 2 (1994): 189+.

IPCC. 2013. Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY. IPCC. 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Janetos, A., L. Hansen, D. Inouye, B.P. Kelly, L. Meyerson, B. Peterson, and R. Shaw. 2008.

Biodiversity. In The effects of climate change on agriculture, land resources, water resources, and biodiversity in the United States. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Washington, DC. 23

Jimenez, R., and J.K. Ladha. 1993. Automated Elemental Analysis - a Rapid and Reliable but Expensive Measurement of Total Carbon and Nitrogen in Plant and Soil Samples.

Communications in Soil Science and Plant Analysis 24, no. 15–16 (1993): 1897–1924.

Jiménez Cisneros, B.E., T. Oki, N.W. Arnell, G. Benito, J.G. Cogley, P. Döll, T. Jiang, and S.S. Mwakalila, 2014: Freshwater resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229-269.

Kaurin, Anela, Rok Mihelic, Damijana Kastelec, Michael Schloter, Marjetka Suhadolc, and Helena Grcman. 2015. “Consequences of Minimum Soil Tillage on Abiotic Soil Properties and Composition of Microbial Communities in a Shallow Cambisol Originated from Fluvioglacial Deposits.” Biology and Fertility of Soils 51, no. 8 (November 2015): 923–33. doi:10.1007/s00374-015-1037-9.

Kay, B. D. 1998. Soil structure and organic carbon: a review. In: Soil Processes and the Carbon Cycle. pp. 169–197, Lal, R., Kimble, J. M., Follet, R. F., and Stewart B. A., Eds., CRC Press, Boca Raton, FL. Kibblewhite, M.G., B.J. Chambers, and K.W.T. Goulding. 2016. How Good Is the Evidence to Support Investment in Soil Protection? Soil Use and Management 32 (June 1, 2016): 172–82.

24

Kieft, Thomas L., Edith Soroker, and Mary K. Firestone. 1987. “Microbial Biomass Response to a Rapid Increase in Water Potential When Dry Soil Is Wetted.” Soil Biology and Biochemistry 19, no. 2 (January 1, 1987): 119–26. doi:10.1016/00380717(87)90070-8.

Keiluweit, Marco, Jeremy J. Bougoure, Peter S. Nico, Jennifer Pett-Ridge, Peter K. Weber, and Markus Kleber. “Mineral Protection of Soil Carbon Counteracted by Root Exudates.” Nature Climate Change 5, no. 6 (June 2015): 588–95. doi:10.1038/nclimate2580.

Kirschbaum, M.U.F. 1995. The Temperature Dependence of Soil Organic Matter Decomposition, and the Effect of Global Warming on Soil Organic C Storage. Soil Biology and Biochemistry 27, no. 6: 753–60.

Kirschbaum, Miko Uwe Franz. 2006. “The Temperature Dependence of Organic-Matter Decomposition—still a Topic of Debate.” Soil Biology and Biochemistry 38, no. 9 (September 2006): 2510–18. doi:10.1016/j.soilbio.2006.01.030.

Kuzyakov, Y. 2006. “Sources of CO2 Efflux from Soil and Review of Partitioning Methods.” Soil Biology and Biochemistry 38, no. 3 (March 2006): 425–48. doi:10.1016/j.soilbio.2005.08.020.

Lal, R. 2004. Soil Carbon Sequestration to Mitigate Climate Change. Geoderma 123, no. 1–2 (November 2004): 1–22.

Lal, R. 2010. Managing Soils and Ecosystems for Mitigating Anthropogenic Carbon Emissions and Advancing Global Food Security. BioScience 60, no. 9 (October 2010): 708–21.

25

Lal, R., J. A. Delgado, P. M. Groffman, N. Millar, C. Dell, and A. Rotz. 2011. Management to Mitigate and Adapt to Climate Change. Journal of Soil and Water Conservation 66, no. 4 (August 2011): 276–85.

Lawrence, Corey R., Jason C. Neff, and Joshua P. Schimel. 2009. “Does Adding Microbial Mechanisms of Decomposition Improve Soil Organic Matter Models? A Comparison of Four Models Using Data from a Pulsed Rewetting Experiment.” Soil Biology and Biochemistry 41, no. 9 (September 2009): 1923–34. doi:10.1016/j.soilbio.2009.06.016.

Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero , L. Bopp, J. Chang, F. Chevallier, L.P. Chini, P. Ciais, M. Fader, R. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A.K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, JEMS Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng. 2015. Global Carbon Budget 2015.

Earth System Science Data.

Leakey, A.D.B. 2006. Photosynthesis, Productivity, and Yield of Maize Are Not Affected by Open-Air Elevation of CO2 Concentration in the Absence of Drought. Plant Physiology 140, no. 2 (January 13, 2006): 779–90.

26

Makipaa, R., M. Hakkinen, P. Muukkonen, and M. Peltoniemi. 2008. The Costs of Monitoring Changes in Forest Soil Carbon Stocks. Boreal Environment Research 13 (2008): 120–30.

Malamoud, K., A.B. McBratney, B. Minasny, and D.J. Field. 2009. Modelling how carbon affects soil structure. Geoderma 149:19–26. doi:10.1016/j.geoderma.2008.10.018.

Manzoni, Stefano, Joshua P. Schimel, and Amilcare Porporato. 2012. “Responses of Soil Microbial Communities to Water Stress: Results from a Meta-Analysis.” Ecology 93, no. 4 (April 1, 2012): 930–38. doi:10.1890/11-0026.1.

Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds. 2014: Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp. doi:10.7930/J0Z31WJ2.

Miller, Amy E., Joshua P. Schimel, Thomas Meixner, James O. Sickman, and John M. Melack. 2005. “Episodic Rewetting Enhances Carbon and Nitrogen Release from Chaparral Soils.” Soil Biology and Biochemistry 37, no. 12 (December 2005): 2195– 2204. doi:10.1016/j.soilbio.2005.03.021.

Min, Kyungjin, Chris Freeman, Hojeong Kang, and Sung-Uk Choi. “The Regulation by Phenolic Compounds of Soil Organic Matter Dynamics under a Changing Environment.” BioMed Research International 2015 (October 1, 2015): e825098. doi:10.1155/2015/825098.

Moinet, Gabriel Y. K., Ellen Cieraad, John E. Hunt, Anitra Fraser, Matthew H. Turnbull, and David Whitehead. 2016. “Soil Heterotrophic Respiration Is Insensitive to Changes in Soil Water Content but Related to Microbial Access to Organic Matter.” Geoderma 274 (July 15, 2016): 68–78. doi:10.1016/j.geoderma.2016.03.027.

27

Moran, K.K., and J.D. Jastrow. 2010. Elevated Carbon Dioxide Does Not Offset Loss of Soil Carbon from a Corn–soybean Agroecosystem. Environmental Pollution, Facing the Future: Evidence from Joint AspenFACE, SoyFACE and SFB 607 Meeting, 158, no. 4 (April 2010): 1088–94.Moyano, Fernando E., Stefano Manzoni, and Claire Chenu.

2013. “Responses of Soil Heterotrophic Respiration to Moisture Availability: An Exploration of Processes and Models.” Soil Biology and Biochemistry 59 (April 2013): 72–85. doi:10.1016/j.soilbio.2013.01.002.

Moyano, Fernando E., Stefano Manzoni, and Claire Chenu. “Responses of Soil Heterotrophic Respiration to Moisture Availability: An Exploration of Processes and Models.” Soil Biology and Biochemistry 59 (April 2013): 72–85. doi:10.1016/j.soilbio.2013.01.002.

Olness, A., and D. Archer. 2005. “Effect of Organic Carbon on Available Water in Soil.”

Soil Science 170, no. 2 (February 2005): 90–101. doi:10.1097/01.ss.0000155496.63323.35.

Osanai, Y., J. K. Janes, P.C.D. Newton, and M.J. Hovenden. 2015. Warming and Elevated CO2 Combine to Increase Microbial Mineralisation of Soil Organic Matter. Soil Biology & Biochemistry 85 (June 2015): 110–18.

Pang, Xueyong, Biao Zhu, Xiaotao Lü, and Weixin Cheng. “Labile Substrate Availability Controls Temperature Sensitivity of Organic Carbon Decomposition at Different Soil Depths.” Biogeochemistry 126, no. 1–2 (November 2015): 85–98. doi:10.1007/s10533015-0141-0.

Paul, K. I., P. J. Polglase, A. M. O’Connell, J. C. Carlyle, P. J. Smethurst, and P. K. Khanna.

2003. “Defining the Relation between Soil Water Content and Net Nitrogen 28

Mineralization.” European Journal of Soil Science 54, no. 1 (March 2003): 39–48. doi:10.1046/j.1365-2389.2003.00502.x.

Paustian, K., J. Lehmann, S. Ogle, D. Reay, G.P. Robertson, and P. Smith. 2016. ClimateSmart Soils. Nature 532, no. 7597: 49–57.

Pereira P., E. Isadora, H. Chung, K. Scow, and J. Six. 2013. Microbial Communities and Soil Structure Are Affected by Reduced Precipitation, but Not by Elevated Carbon Dioxide.

Soil Science Society of America Journal 77, no. 2 (2013): 482.

Pryor, S.C., D. Scavia, C. Downer, M. Gaden, L. Iverson, R. Nordstrom, J. Patz, and G.P. Robertson. 2014. Ch. 18: Midwest. Climate In Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 418-440.

Rawls, W. J., Y. A. Pachepsky, J. C. Ritchie, T. M. Sobecki, and H. Bloodworth. 2003.

“Effect of Soil Organic Carbon on Soil Water Retention.” Geoderma, Quantifying agricultural management effects on soil properties and processes, 116, no. 1–2 (September 2003): 61–76. doi:10.1016/S0016-7061(03)00094-6.

Reeves, D. W. 1997. The Role of Soil Organic Matter in Maintaining Soil Quality in Continuous Cropping Systems. Soil & Tillage Research 43, no. 1–2 (November 1, 1997): 131–67.

Reinsch, Sabine, Per Ambus, Barry Thornton, and Eric Paterson. “Impact of Future Climatic Conditions on the Potential for Soil Organic Matter Priming.” Soil Biology & Biochemistry 65 (October 2013): 133–40. doi:10.1016/j.soilbio.2013.05.013.

29

Schimel, Joshua, Teri C. Balser, and Matthew Wallenstein. 2007. “Microbial Stress-Response Physiology and Its Implications for Ecosystem Function.” Ecology 88, no. 6 (2007): 1386–94.

Schjønning, Per, Ingrid K. Thomsen, Per Moldrup, and Bent T. Christensen. “Linking Soil Microbial Activity to Water- and Air-Phase Contents and Diffusivities.” Soil Science Society of America Journal 67, no. 1 (2003): 156. doi:10.2136/sssaj2003.1560.

Spaccini, R., A. Piccolo, J.S.C. Mbagwu, A.Z. Teshale, and C.A. Igwe. 2002. Influence of the addition of organic residues on carbohydrate content and structural stability of some highland soils in Ethiopia. Soil Use Manage. 18:404–411. doi:10.1079/SUM2002152.

Sulman, Benjamin N., Richard P. Phillips, A. Christopher Oishi, Elena Shevliakova, and Stephen W. Pacala. “Microbe-Driven Turnover Offsets Mineral-Mediated Storage of Soil Carbon under Elevated CO2.” Nature Climate Change 4, no. 12 (December 2014): 1099–1102. doi:10.1038/NCLIMATE2436.

Tisdall, J. M. and Oades, J. M. 1982. Organic matter and water stable aggregates in soils. J. Soil Sci. 33: 141–163.

Uri, N.D. 2001. The Potential Impact of Conservation Practices in US Agriculture on Global Climate Change. Journal of Sustainable Agriculture 18, no. 1 (2001): 109–31. van Groenigen, K.J., J. Six, B.A. Hungate, M. de Graaff, N. van Breemen, and C. van Kessel.

2006. Element Interactions Limit Soil Carbon Storage. Proceedings of the National Academy of Sciences 103, no. 17 (April 25, 2006): 6571–74. van Groenigen, K.J., X. Qi, C. Osenberg, Y. Luo, and B. Hungate. 2014. Faster Decomposition Under Increased Atmospheric CO2 Limits Soil Carbon Storage. Science 344 (May 2, 2014): 508–9.

30

Vörösmarty, Charles J., Pamela Green, Joseph Salisbury, and Richard B. Lammers. 2000.

“Global Water Resources: Vulnerability from Climate Change and Population Growth.” Science 289, no. 5477 (July 14, 2000): 284–88. doi:10.1126/science.289.5477.284.

Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, and R. Somerville. 2014: Ch. 2: Our Changing Climate. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 19-67. doi:10.7930/J0KW5CXT. Wang, T., D.A. Wedin, and V.A. Zlotnik. 2009. Field evidence of a negative correlation between saturated hydraulic conductivity and soil carbon in a sandy soil. Water Resour.

Res. 45:W00A11. doi:10.1029/2007WR006768.

Wieder, William R., Gordon B. Bonan, and Steven D. Allison. “Global Soil Carbon Projections Are Improved by Modelling Microbial Processes.” Nature Climate Change 3, no. 10 (October 2013): 909–12. doi:10.1038/nclimate1951.

Wild, A. 1988. Russell’s soil conditions and plant growth. 11th ed., JohnWiley & Sons, Inc. New York.

Wutzler, T., and M. Reichstein. “Priming and Substrate Quality Interactions in Soil Organic Matter Models.” Biogeosciences 10, no. 3 (2013): 2089–2103. doi:10.5194/bg-10-20892013.

31

Xie, H., K. Liu, D. Sun, Z. Wang, X. Lu, and K. He. 2015. A Field Experiment with Elevated Atmospheric CO2-Mediated Changes to C4 Crop-Herbivore Interactions.

Scientific Reports 5 (September 18, 2015): 13923.

Zhu, Biao, Jessica L. M. Gutknecht, Donald J. Herman, Daniel C. Keck, Mary K. Firestone, and Weixin Cheng. “Rhizosphere Priming Effects on Soil Carbon and Nitrogen Mineralization.” Soil Biology & Biochemistry 76 (September 2014): 183–92. doi:10.1016/j.soilbio.2014.04.033.

Zhuang, J., J.F. McCarthy, E. Perfect, L.M. Mayer, and J.D. Jastrow. 2008. Soil water hysteresis in water-stable microaggregates as affected by organic matter. Soil Sci. Soc. Am. J. 72:212–220. doi:10.2136/sssaj2007.0001S6.

Zong, Y., and Z. Shangguan. 2014. Nitrogen Deficiency Limited the Improvement of Photosynthesis in Maize by Elevated CO2 Under Drought. Journal of Integrative Agriculture 13, no. 1 (January 2014): 73–81.

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Chapter 2: Modeling Soil Organic Carbon in Corn-based Systems in Ohio Under Climate Change 2.1

Abstract Soil organic carbon is a key indicator of soil quality. Knowledge of the effects of land

management and climate change on SOC stocks is of vital importance in creating future sustainable land use systems. This study presents both the promise and current challenges of modeling SOC in mineral soils under climate change. Soils data from two long-term agricultural research sites in Ohio under NT and PT management, the RothC soil C model, and climate data from the Canadian Regional Climate Model were used to project future SOC content in agricultural soils using LE and HE climate change scenarios. It was hypothesized that from 2015 to 2070, SOC levels in soils under NT management in Ohio will show increasing trends under the LE scenario, decreasing trends in NT under the HE scenario, and decreasing trends in PT under both scenarios, with lower levels of SOC for both treatments under the HE scenario. The results of this study projected total SOC content in the topsoil layers (0 to 25 cm [0 to 10 in] at Wooster and 0 to 23 cm [0 to 9 in] at Hoytville) to decrease at all sites and under all management and climate projections, with the exception of NT at Wooster and Hoytville and PT at Wooster under the LE scenario.

Starting at 32.4 Mg C ha-1 (14.5 tn C ac-1) in 1962 at Wooster, by 2070, soil under NT management is projected to have 45.4 and 32.1 Mg C ha-1 (20.3 and 14.3 tn C ac-1) for LE and HE scenarios, respectively, while PT management starting at 31.5 Mg C ha-1 (14.1 tn C

33

ac-1) would have 29.4 and 21.0 Mg C ha-1 (13.1 and 9.4 tn C ac-1) for LE and HE scenarios, respectively. Starting at 65.2 Mg C ha-1 (29.1 tn C ac-1) in 1963 at Hoytville, by 2070, soil under NT management would have 65.9 and 51.0 Mg C ha-1 (29.4 and 22.8 tn C ac-1) for LE and HE scenarios, respectively, and PT starting at 63.5 Mg C ha-1 (28.3 tn C ac-1) would have 36.9 and 28.7 Mg C ha-1 (16.5 and 12.8 tn C ac-1) for LE and HE scenarios, respectively.

2.2

Introduction Soil carbon sequestration is a vital process in the global carbon (C) cycle, particularly

under climate change, and computer modeling of projected C stocks is a key tool for mitigation and adaptation planning.

It is imperative that humanity make rapid and decisive changes to address the global climate change threat. However, mitigation and adaptation methods must be scientifically informed. Sequestration of SOC has been proposed as one tool to mitigate the effects of climate change (Lal 2004a), and was an official proposal at the 21st Conference of the Parties to the United Nations (UN) Framework Convention on Climate Change (COP21), known as “4 Per Thousand.” Current estimates (Lal 2016) indicate that, globally, agriculture has the physical potential to sequester 62 Mg C ha-1 (27 tn C ac-1) over the next 50 to 75 years, with additional potential in pasture, forest, degraded, and desertified lands.

Jones et al. (2005) simulated global-scale soil carbon dynamics with the Hadley Centre’s coupled climate-carbon cycle general circulation model (with a simple single-pool carbon model) and RothC (a more complex multiple-pool carbon model) from 1860 through 2100. Both models calculated soil C increases due to increased plant carbon input from CO2 fertilization until 2000, when the global stocks of C roughly leveled off due to increased plant respiration from increasing temperature. After 2060, both models projected 34

continually-increasing plant respiration to surpass plant carbon inputs, resulting in a dramatic switch of soil as a C sink to a source.

While it is important to understand trends at the global scale, local action requires knowledge of local trends, and global modeling is too general for this purpose. Therefore, the next step in this evaluation is to scale projections down to the regional level with regional climate model data. To date, related agricultural studies in the literature conducted for Ohio or surrounding regions in the Midwestern United States under climate change have primarily focused on crop yields (Littlefield et al. 1998, Southworth et al. 2002, Panagopoulos et al.

2015). Modeling studies have evaluated historical trends in C dynamics (Evrendilek and Wali 2001, Dold et al. 2017) or developed region-predictive models (Mishra et al. 2010) without including future predictive simulations, or included future predictive simulations but with global climate model data (Pan et al. 2010, Lu and Xiaoliang 2010, Basche et al. 2016).

Evrendilek and Wali (2004) projected SOC levels in Ohio under increased CO2 and temperature over 100 years, but the increased values remained constant throughout the simulation and did not include changes in precipitation.

The primary objective of this study is to project future levels of terrestrial C within corn-based agricultural systems in Ohio under both plow-tillage (PT) and no-tillage (zero tillage) (NT) management, and low-emissions (LE) and high-emissions (HE) climate change scenarios (based on varying assumptions of future global GHG emissions) using predictive monthly temperature, precipitation, and open pan evaporation data through the year 2070 produced by a regional-level climate model. Only when there are better models at a local or regional level can scaling up to a larger level landscape provide really useful results.

35

There is a large body of evidence in the literature that NT management results in an increase in surface (0 to 20 cm [0 to 8 in]) SOC under many soil types and climatic conditions, though the effectiveness of NT management over PT to sequester C has been hotly debated (Gregorich et al. 2007; Blanco-Canqui and Lal 2008, Hammons 2009; Powlson et al. 2014; VandenBygaart 2016). The concerns have been addressed in part by two metaanalyses comparing PT to NT, where NT was found to sequester significantly more C than PT to 30 cm (12 in) (Angers and Eriksen-Hamel 2008) and to 160 cm (63 in) (Mangalassery et al. 2015).

Given that NT sequesters more C than PT, that increases in temperature and precipitation are factors that can increase the rate of decomposition, and that increases in both by the end of the century are projected to be substantial across the Midwest, the following hypotheses were tested in this study: 

From 2015 through 2070, levels of SOC in soils under NT management in Ohio will show increasing trends under the LE scenario, decreasing trends under PT management in the LE scenario, and trends of depletion in both treatments under the HE scenario.



Also, by 2070, levels of SOC in soils of both NT- and PT-managed sites in Ohio will be projected to decrease more under a HE than a LE climate change scenario.

2.3

Materials and Methods

2.3.1

Soil Carbon Model There are a wide range of computer simulation models that include a soil C cycle

component, from global climate models to ecosystem models. Several were reviewed for

36

consideration, including Rothamsted Carbon (RothC), Estimate Carbon in Organic Soils – Sequestration and Emissions, Century, DayCent, DeNitrification-DeComposition, and Environmental Policy Integrated Climate. We chose the RothC model, standard version (26.3), for its ease of use, easily-obtainable inputs, usability for most soil types, and high counts of published studies that have used RothC for a purpose similar to this study, which lends credibility to its output. It was developed to model the turnover of SOC in nonwaterlogged topsoils, and runs on a monthly timestep (Coleman and Jenkinson 2014). A model with a smaller timestep was not deemed necessary for this study due to the nature of SOC to change slowly over time, and the exclusion of N, which would require a smaller timestep.

RothC considers the following four active C pools with their own first-order decay rates (given for 9.25oC) (48.7oF): (1) decomposable plant material (DPM) at 10.0 month-1, (2) resistant plant material (RPM) at 0.3 month -1, (3) microbial biomass (BIO) at 0.66 month -1, and (4) humified organic matter (HUM) at 0.02 month -1 (Jones et al. 2005). Each rate is modified as a function, F, of monthly mean surface air temperature (Ta): F

47.91 1 e

(

106.06 ) Ta 18.27

.

The rate of decomposition is also calculated from soil clay content, the presence or absence of vegetation, and the soil moisture deficit (calculated as the difference between total monthly precipitation and open pan evaporation) (Jones et al. 2005).

As discussed in Chapter 1, kinetic theory is a useful framework to evaluate the response of decomposition to temperature change (Conant et al. 2011). RothC accounts for the first component of kinetic theory in that the decomposition rate increases with an 37

increase in temperature. The second component states that the rate of change in decomposition should be the maximum at lower temperatures. In RothC, however, the rate of change of decomposition rates rises until about 26oC (79oF), when the rate of change begins to decrease. The third component of kinetic theory implies that decomposition reactions of slow-pool OM will increase more proportionally than fast-pool OM; in RothC, the same temperature modifying factor is applied to all pools.

2.3.2

Historical Weather Data To fit RothC to observed SOC levels, weather measurements are needed over the

course of the agricultural experiments for monthly mean air temperature, rainfall, and open pan evaporation. Therefore, Annual Climatological Summary data were downloaded from the National Climatic Data Center for the Wooster and Hoytville weather stations corresponding with the agricultural plots used for this study (Menne et al. 2012a, Menne et al. 2012b). Missing open pan evaporation data were calculated using Thornthwaite and Mather’s (1955) calculation for potential evapotranspiration.

2.3.3

Regional Climate Model Climate predictions can be used in conjunction with the RothC model to generate

estimates of future SOC levels. Ideally, predictive climate data for the local proximity of each agricultural experiment site would provide the most relevant data for modeling SOC levels at each individual site. However, robust point estimates of future climate are not available at this high level of resolution. Global circulation models (GCM) could be helpful, but are too low resolution for use at a specific site (Mearns et al. 2013). A regional climate model (RCM) is the best compromise currently available.

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The North American Regional Climate Change Assessment Program (NARCCAP) provides a suite of RCMs. Each model exhibits biases, so the model selected should exhibit the least bias within the factors of interest over the applicable region, in this case temperature and precipitation over Ohio. A careful qualitative analysis of graphical output from each RCM was performed, comparing the observational climate data (limited to 1979 through 2003) from the University of Delaware’s Center for Climatic Research to the output from each RCM’s attempts to reconstruct them. This qualitative analysis consisted of four categories: (1) winter temperature, (2) winter precipitation, (3) summer temperature, and (4) summer precipitation; and three spatial scales: (1) national (United States), (2) regional (Midwest), and (3) state (Ohio). Based on this somewhat subjective analysis, the Canadian Regional Climate Model (CRCM) was selected as the best available fit. Over Ohio, it replicated winter temperatures ±2oC (±3.6oF), winter precipitation from 0-50% increases, summer temperatures -2oC (-3.6oF) to +4oC (+7.2oF), and summer precipitation from -10% to +50% differences (NARCCAP 2016a). Furthermore, to produce the future climate projection data, each RCM is paired with a GCM which provides data for the boundary conditions of each region. The RCM then uses higher-resolution data for factors such as topography, convective precipitation, and local scale soil moisture to produce the projections within the given region. The future climate data chosen for this study from the CRCM was produced as paired with the Third Generation Coupled Global Climate Model (CGCM3), version 3.1 (NARCCAP. 2016c, Mearns et al. 2007). This GCM was used to produce the emissions scenarios for the IPCC’s Fourth Assessment Report (ECCC 2016).

The climate data projected by the CRCM-CGCM3 pairing is among the least variable over

39

the Bukovsky regions relevant to this study (Appalachia [Wooster] and Great Lakes [Hoytville]) (Bukovsky 2011, NARCCAP 2016d).

The original goal of this study was to run projections to the year 2100 from the regional model with simulations equivalent to the IPCC’s global RCP2.6 and RCP8.5 GHG emissions scenarios. (RCP2.6 represents a scenario where global temperature rise is kept below 2o C [3.6o F] over pre-industrial temperatures with an aggressive reduction in global annual GHG emissions to 0 by 2100, while RCP8.5 represents a conversely-aggressive rise in GHG emissions topping out at just over 100 GtCO2/yr [110 billion tn/yr] by 2100 [IPCC 2014].) Several factors prevented this goal. As of the time of our analysis, only a business-asusual HE scenario was available from the NARCCAP program, equivalent to the former IPCC A2 scenario (NARCCAP 2016b.). The IPCC’s A2 scenario represents slightly lower emissions than RCP8.5 (Cubasch et al. 2013). Additionally, the date range of future climate data from the RCMs was limited to 2038 through 2070.

In lieu of a LE scenario from the NARCCAP regional models, a LE scenario was simulated using the 31-year average temperature, precipitation, and open pan evaporation data from the NOAA observed weather data set from 1985 through 2015. This represents the best-case (albeit unrealistic) scenario of no further climate change and serves as a baseline comparison.

2.3.4

Agricultural Sites Detectable changes in SOC only occur over many years, therefore only long-term

agricultural projects that consistently tracked changes in C would yield meaningful results.

This study utilizes data collected from the Triplett-Van Doren experimental plots, located near the cities of Wooster and Hoytville in Ohio. They have been run by the Ohio 40

Agricultural Research and Development Center (OARDC) of The Ohio State University since 1962 and 1963, respectively, through 2010. From 2011-2015 they came under the coordination of the CSCAP (CSCAP 2016). These two sites include the world’s longestrunning NT research plots to date. The Hoytville site has had little change in its treatments since inception, and the Wooster site also had little change in its treatments through 2006 (Dick et al. 2013). Additional details about the site, sampling, and analysis not included in this paper are available in prior publications (Dick et al. 1986a, Dick et al. 1986b, Dick et al.

2013, Kladivko et al. 2014).

The USDA defines Major Land Resource Areas to identify regions with similar characteristics, such as soil, climate, and land use (USDA-NRCS 2005). Hoytville is included in the 28,370 km2 (10,950 mi2) Erie-Huron Lake Plain Major Land Resource Areas, 59% of which is cropped, and which includes parts of Michigan, Indiana, and Ohio (USDA-NRCS 2006a). The Hoytville site’s soil is classified as a silty clay loam and poorly drained with a slope of <1% (Dick et al. 1986b). Drainage tiles were installed in 1952, which predates the experimental period.

Wooster is included in the 27,770 km2 (10,715 mi2) Lake Erie Glaciated Plateau Major Land Resource Areas, 29% of which is cropped, and which includes parts of Ohio, Pennsylvania, and New York (USDA-NRCS 2006b). The Wooster site’s soil is classified as a silty loam. It is deep, well drained, and moderately permeable (Dick et al. 1986a), with a slope varying from 2.5% to 4.0% (Dick 1983) with no mention of drainage tiles. Due to extensive erosion in the Wooster PT plots, they were releveled with added topsoil in 2006 to equate the elevation of the NT plots, effectively ending the PT trial for this study.

41

Tillage systems at both sites include NT, PT, and minimum tillage. For this study, only NT and PT were compared. Tillage and crop rotation management has been parallel between the Hoytville and Wooster sites and continuously maintained since their beginnings, with only minor modifications over time. The three crop rotations are: (1) continuous corn, Table 1. Data collected for the Wooster site. Each value represents either a single sample or an average of multiple samples collected in each given year. Plow-till data from 20112015 were excluded from the study due to site changes after 2005. SOC = soil organic carbon; FD-TOC = Total Organic Carbon calculated to a Fixed Depth; ESM-TOC = Total Organic Carbon calculated to an Equivalent Soil Mass

Year 1962 1971 1979 1980 1993 2003 2005 2011 2013 2015 1971 1979 1980 1993 2003 2005 2011§ 2013§ 2015§

Depth (cm) 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25

Treatment baseline no tillage no tillage no tillage no tillage no tillage no tillage no tillage no tillage no tillage plow tillage plow tillage plow tillage plow tillage plow tillage plow tillage plow tillage plow tillage plow tillage

Bulk Density (Mg/m3 ) SOC% Clay% 1.25 1.02† 16.5 1.39 1.37 — 1.39* 1.24 — 1.40* 1.21 — 1.36 1.30 23.7 1.37 1.23 — 1.53 1.69‡ 14.8 1.46 1.58 — 1.43* 1.21 21.8 1.37 1.34 — 1.39 1.24 — 1.40* 1.05 — 1.40* 1.03 — 1.38 0.95 24.8 1.47 0.93 — 1.66 1.12‡ 14.2 1.40 1.35 — 1.59* 1.34 20.9 1.41 1.40 —

FD-TOC (Mg C ha1 ) 31.9 47.6 43.4 42.1 44.3 42.2 64.6 57.6 43.4 46.0 43.0 36.6 35.9 32.6 34.2 46.2 47.4 53.2 49.4

* Indicates missing data for the sample year. Values were estimated by regression. † SOC% calculated as OM%/1.72. ‡ TC% measured this year. SOC% assumed to equal TC% due to low pH values (<6.2 pH) § Data excluded from study due to post-2006 site changes.

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ESM-TOC (Mg C ha-1) 31.9 42.7 38.8 37.6 40.5 38.2 52.6 49.2 37.7 41.7 38.6 32.7 31.9 29.5 29.0 34.8 42.1 41.7 43.7

(2) corn and soybeans in a 2-year rotation, and (3) corn, oat or hay, and hay in a 3-year rotation (Dick et al. 2013). This study averaged soil data across all crop rotations each for NT and PT treatment as an opportunity to generalize results over some of the most common rotations used in the Midwest, providing a broader application of the results of this study. Both sites are rain-fed with no irrigation.

Table 2. Data collected for the Hoytville site. Each value represents either a single sample or an average of multiple samples collected in each given year. SOC = soil organic carbon; FD-TOC = Total Organic Carbon calculated to a Fixed Depth; ESM-TOC = Total Organic Carbon calculated to a Fixed Depth; ESM-TOC = Total Organic Carbon calculated to an Equivalent Soil Mass

Year 1963 1978 1980 1993 1996 2005 2011 2013 2015 1978 1980 1993 1996 2005 2011 2013 2015

Depth (cm) 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23

Treatment baseline no-till no-till no-till no-till no-till no-till no-till no-till plow-till plow-till plow-till plow-till plow-till plow-till plow-till plow-till

Bulk Density (Mg/m-3) 1.30 1.36* 1.35* 1.35 1.39 1.25 1.31 1.33* 1.38 1.27* 1.27* 1.36 1.25 1.42 1.37 1.39* 1.38

SOC% Clay% 3.31† 37.0 2.31 — 2.77 — 2.63 48.9 2.32 — 2.44‡ 34.8 1.83 — 1.76 48.1 2.28 — 1.99 — 2.00 — 1.95 50.2 1.69 — 1.81‡ 36.8 1.56 — 1.56 49.5 1.66 —

FD-TOC (Mg C ha1) 99.1 72.0 70.7 81.5 73.9 69.8 55.2 53.7 72.6 57.9 58.5 61.1 48.5 59.0 49.3 49.8 52.5

* Indicates missing data for the sample year. Values were estimated by regression. † SOC% calculated as OM%/1.72. ‡ TC% measured this year. SOC% assumed to equal TC% due to low pH values (<=6.5 pH).

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ESM-TOC (Mg C ha1) 99.1 69.1 67.9 78.7 69.3 72.8 54.8 52.6 68.2 59.4 59.7 61.1 50.5 54.1 46.6 46.7 49.5

Soil data usable for this study were collected about every 6-7 years. Samples collected during the experiment were taken in the fall of the year after harvest. For both sites, the baseline year data are from soil survey results taken in the proximity of the plots, but not directly from the plots. At Wooster, the 1962 baseline data set is from a site within 250 m (273 yards) away (Dick et al. 1986a); at Hoytville, the 1963 baseline data set was from a soil survey sample within 1 km (0.6 mile) away (Dick et al. 1986b). This presents a problem for SOC modeling, which will be discussed later.

Soil samples were taken to varying depths in varying increments over the years. The data within each treatment in each site were therefore averaged to the depth chosen for each site. For Wooster (Table 1), the depth chosen was 0 to 25 cm (0 to 10 in). For Hoytville (Table 2), the depth chosen was 0 to 23 cm (0 to 9 in). These depths correspond with the complete topsoil layer at each site. RothC processing is valid to any depth provided it is classified as topsoil (normally 15 to 30 cm [5.9 to 11.8 in]). Since the main purpose of this study is to compare the NT and PT treatments within each site to each other, the small difference in depths between sites is not critical.

The following calculations were used, where measurements were not made directly:

OC% 

OM% (Combs and Nathan 1998), 1.72

FD  TOC  Depth(cm)Yx * BulkDensity Yx * OC Yx % , where FD-TOC is total organic carbon to a fixed depth and the “Yx” subscript refers to each year sampled, and ESM  TOC  Depth(cm)Y 0 * BulkDensity Y 0 * OC Yx % ,

44

where ESM-TOC is total organic carbon in equivalent soil mass and the “Y0” subscript refers to the values from the baseline year. The equivalent soil mass equation (ESM-TOC) was adapted from Lee et al. (2009).

Not all data were collected in each sampling year, so missing data applicable to this study were averaged or estimated. Clay content measurements varied considerably between years within each site and treatment. Because this attribute requires timeframes longer than the experimental period to change significantly, the average percent content was calculated for each site from the values that were collected, in order to use a consistent value with RothC

Figure 6. Wooster bulk density and soil organic carbon contents over time. Dashed lines follow the full set of recorded data from experiment initiation, including initial measurements taken off-site. Solid lines only follow data actually collected from the site, excluding post-2006 samples at the plow-tillage site after field leveling. Round and square data points indicate bulk density and organic C, respectively. Starred (*) points represent bulk density values calculated from the bulk density trend line.

throughout the experimental period: 

Wooster = 19.1%



Hoytville = 42.8%

These values are well within the described soil textures (NRCS 2016) of each site.

SOC% was measured for each sample, with the exception of 2005 when the total carbon percentage (TC%) was measured 45

instead. Due to the consistently low pH values measured in 2005, the SOC% was simply set equal to the TC% (Table 1 and Table 2). Overall, pH values at Wooster ranged from 4.6 to 7.2 (median 6.1) and 4.6 to 7.3 (median 6.1) at NT and PT sites, respectively, and at Hoytville from 5.5 to 7.4 (median 6.8) and 6.0 to 7.2 (median 6.9) at NT and PT sites, respectively.

Missing bulk density values were estimated. An acceptable pedo-transfer function was not available, so existing bulk density data collected from each site and treatment were plotted and values for the missing years were calculated from the trend line equation.

However, difficulties arose when considering the anomalies noted earlier in this paper regarding the initial soil samples in the starting years, as well as the site adjustment to the PT plots at Wooster in 2006. Therefore, the trend line equations chosen for the bulk density estimates were taken from those that represented just the data taken from the sites during the experimental period, excluding post-2006 data for the PT site at Wooster (see Figure 6 for Wooster and Figure 7 for Hoytville), and baseline year data at both sites.

As evident in Table 1, Table 2, Figure 6, and Figure 7, the bulk densities increased over time for PT treatments at both sites and for NT at Wooster. This can present a problem in calculating TOC, as the total mass of soil sampled over time increases as compaction occurs (Lee et al. 2009). To account for this change, the equivalent soil mass was calculated.

2.3.5

Modeling Run Setup The modeling run for this study with RothC involves three steps: (1) establishing

actual or average observed historical values, (2) a best-fit run to align the model with the observed values, and (3) a projection run to model future estimates. It is essential to fit the model to observed historical measurements first to ensure that the model is mimicking the 46

site-specific parameters correctly. Once the model can closely reproduce the observational data, then it can be run forward with expected future values on the various parameters, such as C input quantities and weather data.

Note that the C inputs to RothC are not plant-specific, but rather are generic quantities that are adjustable in order to fit the model to the observed data. Calculating changes in C inputs over time is complicated by biomass increases

Figure 7. Hoytville bulk density and soil organic carbon contents over time. Dashed lines follow the full set of recorded data from experiment initiation, including initial measurements taken off-site. Solid lines only follow data actually collected from the site.

Round and square data points indicate bulk density and organic C, respectively. Starred (*) points represent bulk density values calculated from the bulk density trend line.

due to management factors such as changing cultivars to improved varieties, which occurred occasionally over the experimental period, and decreasing row spacing for corn and soybeans, which occurred in 1972 (Dick et al. 1986a, 1986b). It is reasonable to expect that these variables may change in the future as well, but this study assumes they remain asis from 2015 forward.

47

RothC calculates TOC in two phases: equilibrium and short term. For the model to arrive at the TOC content at equilibrium most accurately, the following information should be known, or estimated as accurately as possible: ď&#x201A;ˇ

Date of land use change when the site was originally converted from a natural ecosystem (and was assumed to be at a state of SOC equilibrium)

ď&#x201A;ˇ

Type of the natural ecosystem (such as forest or grassland)

ď&#x201A;ˇ

Measured TOC content of the natural ecosystem before land use change

The short-term calculation phase adjusts the TOC based on subsequent management and climate changes.

Because the information noted above for the equilibrium phase was unknown for either site, an estimate was made based on the given data and local knowledge about the sites. At Wooster, it was noted that the land was maintained as a grassland for 6 years prior to the beginning of the experimental period (Dick et al. 1986a). Therefore, it was assumed that the site was forest until 1956, grassland until 1961, then converted to cultivation in 1962. At Hoytville, Dick et al. (1986b) note the site was cropped for six years prior to the experimental period. However, they also noted that drainage tiles were installed in 1952, implying the site had been under cultivation for some time prior to that. Also, given the very high initial SOC% in 1963, and communication from Dr. Glover Triplett (one of the founding scientists for the agricultural experiment) indicating that the original ecosystem for this site was forest (personal communication, October 17, 2016), it was assumed this survey sample was taken from a forest site and represents the initial SOC level at equilibrium at this site. This 1963 sample was collected within 1 km (0.6 mi) of the research plots. The soil at

48

the Hoytville site is within a level and broad lake plain area in northwest Ohio. Therefore, little change over distances such as 1 km (0.6 mi) would be expected. Because the TOC levels during the experimental period were considerably lower than this initial measurement, it was assumed that the land had been under cultivation for some time. Therefore, several scenarios with different original land conversion dates were run to find the best fit with the data: 1868, 1915, and 1940. Of the three, 1915 provided the most plausible reproduction of the experimental period and was used as the date to equilibrium and initial land use change.

It was also assumed that conventional PT cropping was practiced from land conversion until the experimental period began in 1963.

For the best-fit runs at Wooster, average weather (NOAA data set, averaged from 1948-1961) was used through equilibrium in 1956. From 1956 through 1961, actual NOAA weather and grassland management were used. From 1962 through 2015, actual NOAA weather and average land management for cultivation (NT or PT) were used. At Hoytville, average weather (NOAA data set, averaged from 1953-1962) was used through equilibrium at 1915. From 1915 to 1953, average weather (1953-1962) and average land management for PT cultivation was used. From 1953 through 1962, actual weather and average PT cultivation was used. From 1963 through 2015, actual weather and average land management (NT or PT) were used.

For the LE projection runs, the same data were used as the best-fit runs (above) through 2015, then NOAA weather data averaged from 1985 through 2015 were used for the projection from 2016 through 2070.

For the HE projection runs, the same data were used as the best-fit runs through 1968. From 1969 through 2000, the CRCM historical reproduction weather data were used.

49

From 2000 through 2037, the average weather from the combined CRCM historical and future data sets were used to fill in the gap until the CRCM future predictive weather data could be used from 2038 through 2070.

2.4

Results and Discussion As noted earlier, there have been a growing number of authors in recent years who

have pointed out limitations to assigning NT as a universal panacea to mitigate climate change through the removal of atmospheric CO2 into long-term storage in the soil (Gregorich et al. 2007; Blanco-Canqui and Lal 2008, Hammons 2009; Powlson et al. 2014; VandenBygaart 2016). Four reasons include (1) the stratification of SOC in the profile of NT soils and its concentration at the surface to 20 cm (8 in) relative to PT soils (Powlson et al. 2014), with the implication that SOC is merely redistributed, rather than accumulated, over time; (2) climatic limitations, such as the cool, moist region of eastern Canada (Gregorich et al. 2007); (3) the definition of â&#x20AC;&#x153;no-tillâ&#x20AC;? itself, which can include occasional tillage (VandenBygaart 2016); and (4) a high spatial variability at the farm scale (BlancoCanqui and Lal 2008). These concerns are specifically addressed in this study as follows: (1) there is evidence of SOC stratification under NT at both sites in this study (data not shown), but total SOC content in the full topsoil layer did increase over time in some situations; (2) the research sites are in a temperate-zone climate, which indicates good potential for SOC sequestration (Lal 2004b); (3) the NT sites in this study were never tilled from initiation; and (4) while spatial variability remains a concern, the temporal scale provides a means to prove the differences between treatments over time.

50

2.4.1

Observed Values Table 1, Table 2, Figure 6, Figure 7, and Figure 8 document and illustrate

considerable variability in measurements over the years, particularly in post-2000 samples. In such long-term studies that span many years and include various people involved in collecting and analyzing samples, variation such as reported here is not surprising. There are many avenues of variability possible. Differences in procedures as seemingly minor as how the surface residue was scraped off can affect the OC measurement. Also, samples were taken to different depths in different years. Stratification of OC and extrapolation calculations can add variability to the results.

At both sites, soil under NT showed higher TOC than that under PT (Figure 8). This corresponds with earlier results published for these same two sites, which found significantly higher levels of TOC under NT as compared to PT, as well as stratification (Dick et al.

1986a, 1986b, 1991). Considering the large body of evidence in the literature that NT methods usually result in higher TOC than PT in the top 20 cm (8 in) of soil, and that PT methods usually result in TOC declines, the corroborative evidence in this study is no surprise. However, that TOC at all sites and treatments in this study are trending downward (Figure 8) with the exception of NT at Wooster is a surprise. Given the relatively level slope of Hoytville (< 1% grade), its losses cannot be attributed to erosion, which could have been a factor at Wooster (2.5% to 4% slope gradient), yet does not appear to be a significant factor in the NT treatment there.

51

Hoytville is virtually level with a high clay content and Wooster has a slope with low clay content. It was expected that Hoytville NT would be increasing in TOC, while PT decreased. Due to the slope and low clay content at Wooster, it was expected that TOC in NT would decrease or hold steady and PT would decrease. These expectations held true for Figure 8. Observed total organic carbon (C) content (corrected to equivalent soil mass) for data actually collected from (a) the Wooster site (3,115 Mg haâ&#x20AC;&#x201C;1 at approximately 25 cm depth) and (b) the Hoytville site (2,990 Mg haâ&#x20AC;&#x201C;1 at approximately 23 cm depth), with trend lines (solid and dashed representing no-till and plowtill, respectively). Wooster plow-till data excludes post-2006 samples at the site after field leveling.

Wooster, but not for Hoytville. Clay affects both the water holding capacity (Williams et al. 1983, Saxton and Rawls 2006) and the proportion of CO2 evolved to biomass and humus formed (Jenkinson et al. 1987, Coleman and Jenkinson 2014). In general, soils with higher clay build up slightly more SOC than those with lower clay, and this could explain the overall difference in TOC content between the sites. However, soils with higher clay hold more water than those with lower clay, which combined with the effects of cultivation management could result in increased decomposition rates of

52

OM and decreasing rates of TOC at Hoytville.

An additional factor may lie in the climate trends over each site. Figure 9 illustrates observed (NOAA) climate changes of a 1.1oC increase in average annual temperature and 27 cm increase in total annual precipitation at Wooster and a 0.75 oC increase in average annual temperature and 11.2 cm increase in total annual precipitation at Hoytville over the duration of their respective experimental periods. Given that increases in temperature

Figure 9. Trends in mean annual temperature (MAT) and total annual precipitation (TP) over Wooster and Hoytville for weather data used to build the low-emissions (observed data) and high-emissions (modeled data) scenarios. Red lines (lower three in each graph) are annual mean temperature. Blue lines (upper three in each graph) are annual total precipitation. Solid lines are from observed (NOAA) values over the duration of the respective experimental periods. Dashed lines are modeled (CRCM historical reproduction) values from 1968-2000. Dash-dot lines are modeled (CRCM future projection) values from 2038-2070.

and precipitation can increase rates of SOC decomposition (Kirschbaum 1995, Cook and Orchard 2008), this could account for some of the overall reductions, or for such modest increases, in TOC at each site, although the effect of temperature on the rate of OM decomposition is a subject of on-going debate (Conant et al. 2011).

53

2.4.2

Best-fit and Projection Runs The left sides of Figure 10 and Figure 11 provide the results of the historical

reconstruction (best-fit) runs through 2015 against the considerable scatter of data points at both sites. The two model runs for this study have a variable degree of correlation with the observed data. While the Hoytville runs show a statistically significant correlation, the Figure 10. Results of modeling runs for Wooster no-till and plow-till sites under low- and highemissions scenarios (upper and lower lines in each graph, respectively) comparing modeled total organic carbon (TOC) to observed TOC, corrected to equivalent soil mass (ESM), which equates to approximately 25 cm (10 in) depth at this site. The dashed vertical line divides the graph into historical reconstruction (best-fit) and projection periods.

Wooster runs do not (Figure 12).

However, the RothC model results reflect the expectation that SOC changes slowly over time. The observed measurements may not be a realistic progression of how C changes in soil, such as about a 14 Mg C ha-1 gain and subsequent loss within the span of a few years (for example, see Table 1, years 2003 to 2005 and 2011 to 2013). RothC will not reflect the high variability apparent in the data collection, resulting in a diminished statistical correlation, but it does capture the overall trend.

The ideal starting point for

RothC to calculate SOC changes in soil is from the point of land use change, such as from 54

native grassland or forest to cultivated field. The model starts its process by calculating the build-up of C levels for the prior 10,000 years leading up to levels measured at the experiment initiation, at which point it assumes the C levels are at equilibrium, and entered into the model as monthly input of plant residues in Mg C ha-1 (Coleman and Jenkinson 2014). Once the modeling run begins, C input levels are entered for each year based either on cropping and yield data, or simply adjusted to best match the TOC measured during the experiment (the latter method was used in this study).

As explained earlier, the actual dates of conversion of the Wooster and Hoytville sites from natural ecosystem to agriculture were

Figure 11. Results of modeling runs for Hoytville no-till and plow-till under low- and highemissions scenarios (upper and lower lines in each graph, respectively) comparing modeled total organic carbon (TOC) to observed TOC, corrected to equivalent soil mass (ESM), which equates to approximately 23 cm (9 in) depth at this site. The dashed vertical line divides the graph into historical reconstruction (best-fit) and projection periods.

estimated. Since the dates of the initial soil samples were set internally in RothC to 1956 and 1915 for Wooster and Hoytville, respectively, the TOC levels that RothC arrived at by each experimental period (Table 3) serve as proxy starting TOC levels for the analysis for this study.

Also explained earlier, the model uses NOAA averaged or observed climate data through 1967, 55

then output from the CRCM historical reconstruction 1968 through 2000. A distinct divergence occurs from 1968 in both sites and treatments with the CRCM model indicating drops in TOC levels from 1968 almost continuously through 2070, resulting in about 29% Table 3. Selected total organic carbon levels generated by the RothC model under lowemissions (LE) and high-emissions (HE) scenarios for no-till (NT) and plow-till (PT) treatments at both agricultural sites, including the calculated difference in modeled carbon levels over the projection period (2015-2070) and over the last two decades (2050-2070). For clarity to compare data to Table 1 and Table 2, data were omitted (with dashes) in the years during the experimental period (1962-2015) that do not correspond to years that physical samples were taken at each site.

LE Scenario Wooster Modeled C (Mg C ha-1)

Hoytville Modeled C (Mg C ha-1)

HE Scenario Wooster Hoytville Modeled C Modeled C (Mg C ha-1) (Mg C ha-1)

Year

NT

PT

NT

PT

NT

PT

NT

PT

1962

32.4

31.5

--

--

32.4

31.5

--

--

1963

--

--

65.2

63.5

--

--

65.2

63.5

1971

38.5

32.4

--

--

35.6

30.4

--

--

58.9

1978

--

--

72.5

--

--

67.2

56.3

1979

38.5

30.9

--

--

35.1

28.7

--

--

1980

37.9

30.5

71.6

57.9

35.0

28.6

67.3

55.6

1993

40.3

30.7

73.9

55.2

34.9

27.1

65.2

50.3

1996

--

--

75.5

55.0

--

--

64.7

49.2

2003

41.6

30.7

--

--

35.0

26.3

--

--

2005

41.3

30.4

72.8

52.1

34.9

26.1

61.7

45.5

2011

40.4

29.5

69.1

49.4

34.9

25.7

60.3

43.3

2013

40.6

29.5

66.7

48.0

34.9

25.5

59.9

42.6

2015

39.8

28.9

64.9

46.8

34.9

25.3

59.6

42.0

2038

43.4

30.0

67.3

42.4

35.3

24.2

55.6

35.4

2050

44.3

29.6

66.8

40.1

35.1

23.5

54.2

32.7

2070

45.4

29.4

65.9

36.9

32.1

21.0

51.0

28.7

5.6

0.5

1.0

-9.9

-2.8

-4.3

-8.6

-13.3

14.1%

1.7%

1.5%

-21.2%

-8.0%

-17.0%

-14.4%

-31.7%

4.5

0.7

1.9

-6.7

0.2

-1.8

-5.4

-9.3

10.3%

2.2%

2.8%

-15.7%

0.5%

-7.6%

-9.8%

-26.2%

1.1

-0.2

-0.9

-3.2

-3.0

-2.5

-3.2

-4.0

2.6%

-0.6%

-1.3%

-8.1%

-8.5%

-10.5%

-5.8%

-12.3%

Difference 2015 to 2070 Difference 2015 to 2050 Difference 2050 to 2070

56

less TOC at Wooster between the LE and HE scenarios for both treatments, and about 22% less TOC at Hoytville between the scenarios for both treatments. The initial drop in TOC levels may be explained by the differences in precipitation between the CRCM and NOAA data. Figure 9 compares the temperature and precipitation over each site by the CRCM model vs. NOAA observed values. The CRCM simulates lower temperatures as compared

Figure 12. Correlation diagrams between observed (equivalent soil mass â&#x20AC;&#x201C; see Table 1 and Table 2) and modeled total organic carbon (TOC) (see Table 3) for Wooster and Hoytville, no-till and plow-till sites, for the best-fit model runs corresponding to the low-emissions scenarios through 2015. Wooster plow-till diagram only includes data through 2005.

to NOAA values, which would indicate a lower rate of decomposition, though both trend upwards. Precipitation simulated by the CRCM is substantially different, however, starting in 1968 with 130.4 cm (51.3 in) total precipitation compared to 87.2 cm (34.3 in) total reported by NOAA and trending slightly downward. With a linear relationship between soil respiration (and therefore organic matter decomposition) and soil water content (Cook and Orchard 2008), moisture plays a significant role in the decomposition of SOC. The right sides of Figure 10 and Figure 11 show the results of the projection runs.

Table 3 shows that three tests resulted in TOC increases over the projection period (2015 57

through 2070): (1) Wooster NT under LE, (2) Wooster PT under LE, and (3) Hoytville NT under LE. Because Wooster NT was trending gently upwards prior to the future projection, this increase was expected. However, the increase for Wooster PT was not expected. All remaining treatment and scenario combinations resulted in TOC decreases, as expected.

However, these results are based on the total increases or decreases over the total projection period. These increases only hold through 2050. From 2050 through 2070, both treatments at both sites – with the exception of NT at Wooster under LE – show a decreasing trend in TOC (Table 3).

The LE scenario assumes climate change halts at the 1985 to 2015 average, so the modest gains (or minimal loss) indicated in Figure 10 and Figure 11 are unlikely. The results of the HE scenario are most likely influenced by the heavy precipitation increases predicted by the CRCM model in both summer and winter, as well as increases in temperature (Figure 9). After the CRCM projected weather data begins in 2038, the Wooster site begins to show a small climate signal from 2050 to 2070 with hints of the drop in TOC projected by Jones et al. (2005) using RothC and the HadCM3 GCM. The scenarios as constructed in this study have some attributes that are unrealistic, but both are legitimate for the purpose they serve, which is to delineate the most likely upper and lower bounds on the trend of SOC accumulation/loss over time. For example, LE represents a best-case scenario (“What if climate change stopped at the current 31-year average?”). Though unrealistic, it serves as a projected baseline to compare to HE, which represents the worst-case scenario (“What if emissions continue to increase?”), which is actually not at all unrealistic (IPCC 2013). But the regional climate model chosen (in fact, all in the NARCCAP project) also has a higher temperature and precipitation bias over 58

observed values for this specific region, which exaggerates the already worst-case scenario, but therefore serves its purpose as a lower bound.

Mearns et al. (2013) explain the status of RCM consistency and reliability as follows.

All RCMs show biases. In general, GCM and RCM precipitation projections for the winter season over the eastern portion of the Midwest, including Ohio, are in agreement. However, there is no agreement between GCM and RCM projections of summer precipitation over the Midwest as a whole. The Great Lakes region presents a particular challenge for RCMs, due to issues representing the lakes, or to errors related to the setup of the lake models (Mearns et al. 2013).

Although higher precipitation amounts are projected into the future by the CRCM model for Ohio, GCMs are projecting that much of this precipitation increase will occur in heavier events (Melillo et al. 2014, Collins et al. 2013). A result could be drier soils overall in the top 10 cm (4 in) between these heavy events (Collins et al. 2013). Periods of higher temperatures and less soil moisture would indicate a slowing effect on decomposition rates, and this might well be the case in soil under natural ecosystems. However, in soils of agroecosystems, crops may need more water to survive than natural vegetation. If drier periods in summer would lead to irrigation additions, resulting in even higher moisture inputs over the course of the year, this could accelerate decomposition further. Additional irrigation can be accounted for in the input files to RothC. However, RothC runs on a monthly timestep and, therefore, only calculates decomposition based on total moisture input for the month and cannot capture daily fluctuations. Continued research into methods that can cool the soil surface, increase water infiltration rates, and strengthen aggregate stability such as mulching and cover crops (Hobbs et al. 2008, Lal 2016) are and will be 59

needed. Modeling studies of these methods will also be particularly necessary to predict their future effectiveness under a changing climate.

2.5

Summary and Conclusions With climate change threatening to be a substantial challenge to future soil health, this

study sought to determine how SOC levels will respond to climate change. We observe and conclude the following: 

From 2015 through 2070, SOC content in the full topsoil layers is projected to decrease under all sites and management in this study with the exception of NT at Wooster and Hoytville and PT at Wooster under the LE scenario, and final SOC content is projected to be lower under a HE than a LE scenario.



Additionally, all sites and treatments, with the exception of NT at Wooster under LE, show decreasing trends of SOC over the second half of the projection period (2050 to 2070) which is in general agreement with projections by Jones et al. (2005).



We feel with reasonable confidence that the upper limit of SOC content predicted by the LE scenario will not be exceeded given the parameters of this study, and most likely not even attained due to the high certainty by climate scientists that the effects of climate change will continue over the time period in this study, though the effects of eCO2 remain uncertain.



The accuracy of the lower limit predicted by the HE scenario is suspect due to the evident inability of the CRCM to replicate historical climate conditions local to the two sites, and so we believe this prediction to be too low. Therefore, we expect with

60

reasonable confidence that the actual future levels of SOC will fall within the range bounded by the LE and HE predicted levels. ď&#x201A;ˇ

Although there is some uncertainty integrated into the results of this study, observed values are already indicating downward trends in SOC in recent years, many of which are among the warmest on record globally (Cole and McCarthy 2015) and under increases in local precipitation trends.

ď&#x201A;ˇ

Extrapolation from this study suggests that some current farming practices, on soils and in climates similar to those at the Wooster and Hoytville sites, may not be able to be sustained in the long-term under climate change. Combined, these sites are representative of around 24,000 km2 (around 9000 mi2) of cropped agricultural land in the United States across the northern regions of the Midwest and into the Northeast.

ď&#x201A;ˇ

For more effective future modeling including climate change, regional models should 1) provide projections that match IPCC LE and HE scenarios through 2100, 2) improve precipitation projections, and 3) provide continuous data from historical reconstructions through future projections. Additionally, SOC models should incorporate the effects of eCO2 with increases both of plant-based C inputs and its decomposition.

2.6

Acknowledgements Significant gratitude is extended to Dr. Rattan Lal, Dr. Kevin Coleman, Dr. Alvaro

Montenegro, and Dr. Warren Dick for their contributions to, and expert editing of, this

61

paper. Their substantial input and support are why this chapter was accepted for publication to the Journal of Soil and Water Conservation.

Deep thanks are also extended to the Carbon Management and Sequestration Center at the Ohio State University in Columbus, Ohio, and to the following individuals who provided expertise and general assistance: Dr. Jose Guzman, Chris Eidson, Dr. Pat Bell, Nall Moonilall, Reed Johnson, Laura Conover, and Dirk Maas.

2.7

References

Abebe, A., H. Pathak, S.D. Singh, A. Bhatia, R.C. Harit, and V. Kumar. 2016. Growth, Yield and Quality of Maize with Elevated Atmospheric Carbon Dioxide and Temperature in North–west India. Agriculture, Ecosystems & Environment 218 (February 15, 2016): 66–72.

Angers, D. A., and N. S. Eriksen-Hamel. 2008. Full-Inversion Tillage and Organic Carbon Distribution in Soil Profiles: A Meta-Analysis. Soil Science Society of America Journal 72, no. 5 (October 2008): 1370–74.

Attavanich, W., and B.A. McCarl. 2014. How Is CO2 Affecting Yields and Technological Progress? A Statistical Analysis. Climatic Change 124, no. 4 (June 2014): 747–62.

Basche, A.D., S.V. Archontoulis, T.C. Kaspar, D.B. Jaynes, T.B. Parkin, and F.E. Miguez.

2016. Simulating Long-Term Impacts of Cover Crops and Climate Change on Crop Production and Environmental Outcomes in the Midwestern United States. Agriculture, Ecosystems & Environment 218 (February 2016): 95–106.

Blanco-Canqui, H., and R. Lal. 2008. No-Tillage and Soil-Profile Carbon Sequestration: An on-Farm Assessment. Soil Science Society of America Journal 72, no. 3 (June 2008): 693–701.

62

Bukovsky, M.S., 2011: Masks for the Bukovsky regionalization of North America, Regional Integrated Sciences Collective, Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO. Accessed 2016-10-03. [http://www.narccap.ucar.edu/contrib/bukovsky/] Carney, K.M., B.A. Hungate, B.G. Drake, and J.P. Megonigal. 2007. Altered Soil Microbial Community at Elevated CO2 Leads to Loss of Soil Carbon. Proceedings of the National Academy of Sciences of the United States of America 104, no. 12 (March 20, 2007): 4990â&#x20AC;&#x201C;95.

Cheng, L., F.L. Booker, C. Tu, K.O. Burkey, L. Zhou, H.D. Shew, T.W. Rufty, and S. Hu. 2012. Arbuscular Mycorrhizal Fungi Increase Organic Carbon Decomposition Under Elevated CO2. Science 337 (August 31, 2012): 1084â&#x20AC;&#x201C;87.

Cole, S., and L. McCarthy. 2015. 2014 Warmest Year in Modern Record. Climate Change: Vital Signs of the Planet, January 16, 2015. http://climate.nasa.gov/news/2221/2014warmest-year-in-modern-record/.

Coleman, K., and D.S. Jenkinson. 2014. RothC - A Model for the Turnover of Carbon in Soil. Harpenden, UK: Rothamsted Research.

Collins, M., R. Knutti, J. Arblaster, J.L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver and M. Wehner.

2013. Long-term Climate Change: Projections, Commitments and Irreversibility. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y.

63

Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Combs, S.M., and M.V. Nathan. 1998. Ch. 12: Soil Organic Matter. In Recommended Chemical Soil Test Procedures for the North Central Region. Missouri Agricultural Experiment Station Bulletin 1001.

Conant, R.T., M.G. Ryan, G.I. Ågren, H.E. Birge, E.A. Davidson, P.E. Eliasson, S.E. Evans, S.D. Frey, C.P. Giardina, F.M. Hopkins, R. Hyvonen, M.U.F. Kirschbaum, J.M. Lavallee, J. Liefeld, W.J. Parton, J.M. Steinweg, M.D. Wallenstain, J.A.M. Wetterstedt, and M.A. Bradford. 2011. Temperature and Soil Organic Matter Decomposition Rates Synthesis of Current Knowledge and a Way Forward. Global Change Biology 17, no. 11 (November 2011): 3392–3404.

Cook, F.J., and V.A. Orchard. 2008. Relationships between Soil Respiration and Soil Moisture. Soil Biology & Biochemistry 40, no. 5 (May 2008): 1013–18.

CSCAP (Cropping Systems Coordinated Agricultural Project). 2016. About. Sustainable Corn.Org - Crops, Climate, Culture and Change. http://www.sustainablecorn.org/About-Project/Index.html.

Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald, and J.-G. Winther. 2013. Introduction. In Climate Change 2013: The Physical Science Basis.

Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

64

Dick, W.A. 1983. Organic carbon, nitrogen, and phosphorus concentrations and pH in soil profiles as affected by tillage intensity. Soil Science Society of America Journal 47:102107.

Dick, W.A., D.M. Van Doren, Jr., G.B. Triplett, Jr., and J.E. Henry. 1986a. Influence of long-term tillage and rotation combinations on crop yields and selected soil parameters. II. Results obtained for a Typic Fragiudalf soil. Research Bulletin 1181.

Wooster, OH: Ohio Agricultural Research and Development Center, The Ohio State University.

Dick, W.A., D.M. Van Doren, Jr., G.B. Triplett, Jr., and J.E. Henry. 1986b. Influence of long-term tillage and rotation combinations on crop yields and selected soil parameters. I. Results obtained for a Mollic Ochraqualf soil. Research Bulletin 1180.

Wooster, OH: Ohio Agricultural Research and Development Center, The Ohio State University.

Dick, W.A., E.L. McCoy, W.M. Edwards, and R. Lal. 1991. Continuous application of notillage to Ohio soils. Agronomy Journal 83:65-73.

Dick, W.A., Triplett, G.B., and D.M. Van Doren. 2013. Triplett-Van Doren Long-term Tillage and Crop Rotation Data. Wooster, OH: Ohio Agricultural Research and Development Center, The Ohio State University. http://hdl.handle.net/1811/55716 Dijkstra, F.A., and J.A. Morgan. 2012. Elevated CO2 and Warming Effects on Soil Carbon Sequestration and Greenhouse Gas Exchange in Agroecosystems. In Managing Agricultural Greenhouse Gases, 467â&#x20AC;&#x201C;86. Elsevier, 2012. http://linkinghub.elsevier.com/retrieve/pii/B9780123868978000279.

65

Dold, C., H. Büyükcangaz, W. Rondinelli, J.H. Prueger, T.J. Sauer, and J.L. Hatfield. 2017.

Long-Term Carbon Uptake of Agro-Ecosystems in the Midwest. Agricultural and Forest Meteorology 232 (January 2017): 128–40.

ECCC (Environment and Climate Change Canada). 2016. The Third Generation Coupled Global Climate Model. ECCC. http://www.ec.gc.ca/ccmaccccma/default.asp?lang=En&n=1299529F-1 Evrendilek, F., and M.K. Wali. 2001. Modelling Long-Term C Dynamics in Croplands in the Context of Climate Change: A Case Study from Ohio. Environmental Modelling & Software 16, no. 4 (June 2001): 361–75.

Evrendilek, F., and M.K. Wali. 2004. Changing Global Climate: Historical Carbon and Nitrogen Budgets and Projected Responses of Ohio’s Cropland Ecosystems.

Ecosystems 7, no. 4 (June 2004).

Gregorich, E.G., P. Rochette, A.J. VandenBygaart, and D.A. Angers. 2007. Greenhouse Gas Contributions of Agricultural Soils and Potential Mitigation Practices in Eastern Canada (vol 83, Pg 53, 2005). Soil & Tillage Research 94, no. 1 (May 2007): 262–63.

Hammons, S.K. 2009. Re-Examining the Relationship between Tillage Regime and Global Climate Change. Journal of Natural Resources and Life Sciences Education 38 (2009): 144–47.

Hatfield, J. 2012. Agriculture in the Midwest. In U.S. National Climate Assessment Midwest Technical Input Report. J. Winkler, J. Andresen, J. Hatfield, D. Bidwell, and D. Brown, coordinators. Available from the Great Lakes Integrated Sciences and Assessments (GLISA) Center, http://glisa.msu.edu/docs/NCA/MTIT_Agriculture.pdf

66

Hatfield, J., G. Takle, R. Grotjahn, P. Holden, R.C. Izaurralde, T. Mader, E. Marshall, and D. Liverman. 2014. Ch. 6: Agriculture. In Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 150-174.

Hobbs, P.R., K. Sayre, and R. Gupta. 2008. The Role of Conservation Agriculture in Sustainable Agriculture. Philosophical Transactions of the Royal Society of London B: Biological Sciences 363, no. 1491 (February 12, 2008): 543â&#x20AC;&#x201C;55.

Hopkins, F.M., T.R. Filley, G. Gleixner, M. Lange, S.M. Top, and S.E. Trumbore. 2014.

Increased Belowground Carbon Inputs and Warming Promote Loss of Soil Organic Carbon through Complementary Microbial Responses. Soil Biology & Biochemistry 76 (September 2014): 57â&#x20AC;&#x201C;69.

IPCC (Intergovernmental Panel on Climate Change). 2013. Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY. IPCC. 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Janetos, A., L. Hansen, D. Inouye, B.P. Kelly, L. Meyerson, B. Peterson, and R. Shaw. 2008.

Biodiversity. In The effects of climate change on agriculture, land resources, water 67

resources, and biodiversity in the United States. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Washington, DC. Jenkinson, D.S., P.B.S Hart, J.H. Rayner, and L.C. Parry. 1987. Modelling the turnover of organic matter in long-term experiments at Rothamsted. INTECOL Bulletin 15, 1-8.

Jimenez, R., and J.K. Ladha. 1993. Automated Elemental Analysis - a Rapid and Reliable but Expensive Measurement of Total Carbon and Nitrogen in Plant and Soil Samples.

Communications in Soil Science and Plant Analysis 24, no. 15–16 (1993): 1897–1924.

Jones, C., C. McConnell, K. Coleman, P. Cox, P. Falloon, D. Jenkinson, and D. Powlson.

2005. Global Climate Change and Soil Carbon Stocks; Predictions from Two Contrasting Models for the Turnover of Organic Carbon in Soil. Global Change Biology 11, no. 1: 154–66.

Kaurin, A., R. Mihelič, D. Kastelec, M. Schloter, M. Suhadolc, and H. Grčman. 2015.

Consequences of Minimum Soil Tillage on Abiotic Soil Properties and Composition of Microbial Communities in a Shallow Cambisol Originated from Fluvioglacial Deposits.

Biology and Fertility of Soils 51, no. 8 (November 2015): 923–33.

Kibblewhite, M.G., B.J. Chambers, and K.W.T. Goulding. 2016. How Good Is the Evidence to Support Investment in Soil Protection? Soil Use and Management 32 (June 1, 2016): 172–82.

Kirschbaum, M.U.F. 1995. The Temperature Dependence of Soil Organic Matter Decomposition, and the Effect of Global Warming on Soil Organic C Storage. Soil Biology and Biochemistry 27, no. 6: 753–60.

Kladivko, E.J., M.J. Helmers, L.J. Abendroth, D. Herzmann, R. Lal, M. Castellano, D.S. Mueller, J.E. Sawyer, R.P. Anex, R.W. Arritt, B. Basso, J.V. Bonta, L. Bowling, R.M. 68

Cruse, N.R. Fausey, J. Frankenberger, P. Gassman, A.J. Gassmann, C.L. Kling, A. Kravchenko, J.G. Lauer, F.E. Miguez, E.D. Nafziger, N. Nkongolo, M. O'Neal, L.B. Owens, P. Owens, P. Scharf, M.J. Shipitalo, J.S. Strock, and M.B. Villamil. 2014.

Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, Special Issue for Climate and Agriculture. 69(6):532-542. http://dx.doi.org/10.2489/jswc.69.6.532 Lal, R. 2004a. Soil Carbon Sequestration to Mitigate Climate Change. Geoderma 123, no. 1– 2 (November 2004): 1–22.

Lal, R. 2004b. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science 304, no. 5677 (June 11, 2004): 1623–27.

Lal, R. 2010. Managing Soils and Ecosystems for Mitigating Anthropogenic Carbon Emissions and Advancing Global Food Security. BioScience 60, no. 9 (October 2010): 708–21.

Lal, R., J. A. Delgado, P. M. Groffman, N. Millar, C. Dell, and A. Rotz. 2011. Management to Mitigate and Adapt to Climate Change. Journal of Soil and Water Conservation 66, no. 4 (August 2011): 276–85.

Lal, R. 2016. Beyond COP 21: Potential and Challenges of the “4 per Thousand” Initiative.

Journal of Soil and Water Conservation 71, no. 1 (January 1, 2016): 20A–25A. Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero , L. Bopp, J. Chang, F. Chevallier, L.P. Chini, P. Ciais, M. Fader, R. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. 69

Ilyina, A.K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, JEMS Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng. 2015. Global Carbon Budget 2015.

Earth System Science Data.

Leakey, A.D.B. 2006. Photosynthesis, Productivity, and Yield of Maize Are Not Affected by Open-Air Elevation of CO2 Concentration in the Absence of Drought. Plant Physiology 140, no. 2 (January 13, 2006): 779–90.

Lee, J., J.W. Hopmans, D.E. Rolston, S.G. Baer, and J. Six. 2009. Determining Soil Carbon Stock Changes: Simple Bulk Density Corrections Fail. Agriculture, Ecosystems & Environment 134, no. 3–4 (December 2009): 251–56.

Littlefield, B. Z., J. L. Ehman, W. Fan, J. J. Johnston, B. Offerle, and J. C. Randolph. 1998.

Climate Change Effects on Agricultural Productivity in the Midwestern Great Lakes Region. Edited by S. A. ElSwaify and D. S. Yakowitz. Boca Raton: Lewis Publishers Inc, 1998.

Lu, X., and Q. Zhuang. 2010. Evaluating Climate Impacts on Carbon Balance of the Terrestrial Ecosystems in the Midwest of the United States with a Process-Based Ecosystem Model. Mitigation and Adaptation Strategies for Global Change 15, no. 5 (June 2010): 467–87.

70

Makipaa, R., M. Hakkinen, P. Muukkonen, and M. Peltoniemi. 2008. The Costs of Monitoring Changes in Forest Soil Carbon Stocks. Boreal Environment Research 13 (2008): 120–30.

Mangalassery, S., S. Sjoegersten, D.L. Sparkes, and S.J. Mooney. 2015. Examining the Potential for Climate Change Mitigation from Zero Tillage. Journal of Agricultural Science 153, no. 7 (September 2015): 1151–73.

Mearns, L.O., S. McGinnis, S. Biner, D. Caya, and R. Laprise. 2007, updated 2014. The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2016-05-05.

Mearns, L. O., S. Sain, L.R. Leung, M.S. Bukovsky, S. McGinnis, S. Biner, D. Caya, R.W. Arritt, W. Gutowski, E. Takle, M. Snyder, R.G. Jones, A.M.B. Nunes, S. Tucker, D. Herzmann, L. McDaniel, L. Sloan. 2013. Climate Change Projections of the North American Regional Climate Change Assessment Program (NARCCAP). Climatic Change 120, no. 4 (October 2013): 965–75.

Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. 2014. Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp.

Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston. 2012a. Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. Annual Summaries of the Global Historical Climatology Network - Daily. NOAA National Climatic Data Center. October 8, 2015.

71

Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston. 2012b. An Overview of the Global Historical Climatology Network-Daily Database. J. Atmos. Oceanic Technol., 29, 897-910.

Mishra, U., R. Lal, D. Liu, and M. Van Meirvenne. 2010. Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale. Soil Science Society of America Journal 74, no. 3 (2010): 906.

Moran, K.K., and J.D. Jastrow. 2010. Elevated Carbon Dioxide Does Not Offset Loss of Soil Carbon from a Corn–soybean Agroecosystem. Environmental Pollution, Facing the Future: Evidence from Joint AspenFACE, SoyFACE and SFB 607 Meeting, 158, no. 4 (April 2010): 1088–94.

NARCCAP (North American Regional Climate Change Assessment Program). 2016a.

NCEP Seasonal Climate. NARCCAP. http://www.narccap.ucar.edu/results/ncepresults.html.

NARCCAP. 2016b. The A2 Emissions Scenario. NARCCAP. http://www.narccap.ucar.edu/about/emissions.html.

NARCCAP. 2016c. Atmosphere-Ocean General Circulation Models (AOGCMs).

NARCCAP. http://www.narccap.ucar.edu/about/aogcms.html NARCCAP. 2016d. Scatter Plots. http://narccap.ucar.edu/results/scatter/index.html.

NRCS (National Resources Conservation Service). 2016. Soil Texture Calculator | NRCS Soils. NRCS. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_0541 67.

72

Osanai, Y., J. K. Janes, P.C.D. Newton, and M.J. Hovenden. 2015. Warming and Elevated CO2 Combine to Increase Microbial Mineralisation of Soil Organic Matter. Soil Biology & Biochemistry 85 (June 2015): 110–18.

Pan, Z., D. Andrade, M. Segal, J. Wimberley, N. McKinney, and E. Takle. 2010. Uncertainty in Future Soil Carbon Trends at a Central U.S. Site under an Ensemble of GCM Scenario Climates. Ecological Modelling 221, no. 5 (March 2010): 876–81.

Panagopoulos, Y., P.W. Gassman, R.W. Arritt, D.E. Herzmann, T.D. Campbell, A. Valcu, M.K. Jha, C.L. Kling, R. Srinivasan, M. White, and J.G. Arnold. 2015. Impacts of Climate Change on Hydrology, Water Quality and Crop Productivity in the OhioTennessee River Basin. International Journal of Agricultural and Biological Engineering 8, no. 3 (2015): 1–18.

Paustian, K., J. Lehmann, S. Ogle, D. Reay, G.P. Robertson, and P. Smith. 2016. ClimateSmart Soils. Nature 532, no. 7597: 49–57.

Pereira P., E. Isadora, H. Chung, K. Scow, and J. Six. 2013. Microbial Communities and Soil Structure Are Affected by Reduced Precipitation, but Not by Elevated Carbon Dioxide.

Soil Science Society of America Journal 77, no. 2 (2013): 482.

Powlson, D.S., C.M. Stirling, M.L. Jat, B.G. Gerard, C.A. Palm, P.A. Sanchez, and K.G. Cassman. 2014. Limited Potential of No-till Agriculture for Climate Change Mitigation.

Nature Climate Change 4, no. 8 (August 2014): 678–83.

Pryor, S.C., D. Scavia, C. Downer, M. Gaden, L. Iverson, R. Nordstrom, J. Patz, and G.P. Robertson. 2014. Ch. 18: Midwest. Climate In Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 418-440.

73

Reeves, D. W. 1997. The Role of Soil Organic Matter in Maintaining Soil Quality in Continuous Cropping Systems. Soil & Tillage Research 43, no. 1–2 (November 1, 1997): 131–67.

Saxton, K. E., and W. J. Rawls. 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Science Society of America Journal 70, no. 5 (October 2006): 1569–78.

Southworth, J, R. Pfeifer, M. Habeck, J. Randolph, O. Doering, and D. Rao. 2002.

Sensitivity of Winter Wheat Yields in the Midwestern United States to Future Changes in Climate, Climate Variability, and CO2 Fertilization. Climate Research 22 (2002): 73– 86.

Thornthwaite, C.W. and J.R. Mather. 1955. The Water Balance. Publications in Climatology, Vol 8, No. 1, Centerton, N.J., Laboratory of Climatology.

Uri, N.D. 2001. The Potential Impact of Conservation Practices in US Agriculture on Global Climate Change. Journal of Sustainable Agriculture 18, no. 1 (2001): 109–31.

USDA-NRCS. 2005. MLRA Definitions | NRCS Soils. December 15, 2005. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_ 053625 USDA-NRCS. 2006a. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: L - Lake State Fruit, Truck Crop, and Dairy Region; 99 - Erie-Huron Lake Plain. Major Land Resource Regions Custom Report, 2006. http://apps.cei.psu.edu/mlra/.

USDA-NRCS. 2006b. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: R - Northeastern Forage and Forest 74

Region 139 - Lake Erie Glaciated Plateau. Major Land Resource Regions Custom Report, 2006. http://apps.cei.psu.edu/mlra/.

VandenBygaart, A.J. 2016. The Myth That No-till Can Mitigate Global Climate Change.

Agriculture Ecosystems & Environment 216 (January 15, 2016): 98–99. van Groenigen, K.J., J. Six, B.A. Hungate, M. de Graaff, N. van Breemen, and C. van Kessel.

2006. Element Interactions Limit Soil Carbon Storage. Proceedings of the National Academy of Sciences 103, no. 17 (April 25, 2006): 6571–74. van Groenigen, K.J., X. Qi, C. Osenberg, Y. Luo, and B. Hungate. 2014. Faster Decomposition Under Increased Atmospheric CO2 Limits Soil Carbon Storage. Science 344 (May 2, 2014): 508–9.

Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, and R. Somerville. 2014. Ch. 2: Our Changing Climate. In Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 19-67.

Williams, J., R.E. Prebble, W.T. Williams, and C.T. Hignett. 1983. The Influence of Texture, Structure and Clay Mineralogy on the Soil Moisture Characteristic. Australian Journal of Soil Research 21, no. 1 (1983): 15.

Xie, H., K. Liu, D. Sun, Z. Wang, X. Lu, and K. He. 2015. A Field Experiment with Elevated Atmospheric CO2-Mediated Changes to C4 Crop-Herbivore Interactions.

Scientific Reports 5 (September 18, 2015): 13923.

75

Zong, Y., and Z. Shangguan. 2014. Nitrogen Deficiency Limited the Improvement of Photosynthesis in Maize by Elevated CO2 Under Drought. Journal of Integrative Agriculture 13, no. 1 (January 2014): 73â&#x20AC;&#x201C;81.

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Chapter 3: Modeling Soil Organic Matter and Soil Moisture Retention 5.1

Abstract Soil is the foundation of civilization and all terrestrial life, and water is the lifeblood.

Thus, soil degradation through depletion of OM and the attendant decline in plant available water capacity (AWC), adversely affects civilization and all terrestrial life. Soils most vulnerable to degradation processes are those of agroecosystems which are also the most sensitive to changes in the water balance. Arguably, one of the most important functions of soil is its facilitation of water processes, which has a direct relationship with soil OM content. The capacity of soil to hold water is determined largely by physical properties, of which OM is a key determining factor, and therefore its loss in soil can affect a soilâ&#x20AC;&#x2122;s capacity to store water. The objectives of this meta-analysis are to determine if OM and AWC are significantly correlated in soils of several counties in Ohio, and to develop a model that explains the relationship. This study found a significant, if weak, correlation when viewed in the context of other influential factors, though bulk density was the strongest individual indicator. The strongest predictor of AWC was a combination of multiple factors, namely bulk density, clay, and OM contents. A weak, significant correlation (R2 = 0.10) was found with a polynomial model that indicated an increase in AWC with OM until about 3% OM, then a downward trend through the limit of the data at 6.9% OM. Strong, significant correlations were found, however, between field capacity (R2 = 0.68) and the permanent

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wilting point (R2 = 0.95). Due to multiple issues with the datasets employed, however, additional studies are needed with strong data sets from diverse soils of the Midwestern USA. 5.2

Introduction Soil and water are fundamental to human survival. These two vital components are

inexorably linked in the global processes that regulate earthâ&#x20AC;&#x2122;s environment and support its life. Without soil, the terrestrial food chain as we know it would not exist. Entire civilizations have collapsed because they had exhausted their soils and mismanaged their water (Gomiero 2016). Soil provides ecosystem services to all terrestrial life, such as the provision of food, fiber, and fuel; water capture, cleansing, and storage; as well as streamflow regulation (Lal et al. 2013, FAO 2015) and drought alleviation.

Land management practices are only sustainable if key soil functions do not degrade (Deurer et al. 2008). A principal factor in processes of soil degradation is organic matter (OM) content, which also influences many physical and chemical properties (Stocking et al.

2005, Lal 2014), including soil water retention. Alterations in OM content due to management and climate change could affect a soilâ&#x20AC;&#x2122;s ability to collect and store water, and thus increase the risks of drought associated with extreme events.

Soil water retention relates to the physical processes (adhesion, cohesion, and capillary action) by which soil retains water after infiltration, and is influenced by chemical and biological processes. A soilâ&#x20AC;&#x2122;s ability to retain water directly affects the total quantity of water available for plant uptake, which is the water remaining after the initial gravitational drainage (field capacity [FC]) until the only water left is too tightly bound to soil particles for

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plant use (the permanent wilting point [PWP]), which is the available water capacity (AWC) (Lal and Shukla 2004). Thus, AWC is defined mathematically in equation 1: AWC = FC – PWP

(1)

The capacity of soil to hold water, as well as the amount it actually contains, is determined largely by physical properties, of which OM is a key determining factor (FAO 2015, Blanco-Canqui and Benjamin 2013, Rajan et al. 2010). However, the nature of soil minerals to retain water prevents “catastrophic” losses in water with large losses of OM content (Olness and Archer 2005). The latter sustains key soil functions that promote soil aggregation and water infiltration by supplying the energy and substrates needed to support the diversity and activity of soil microbes to maintain their own environment (Franzluebbers 2002).

OM is about 58% soil organic carbon (OC) (Combs and Nathan 1998). Although recent studies have published this content as 50% (Pribyl 2010), this not yet widely accepted.

The OC component of OM has a stronger effect than does clay on pore volume, water retention, and soil structure (Boivin et al. 2009), specific surface area, and water adsorption capacity (Rawls et al. 2003). It influences soil in many additional ways which affect its ability to infiltrate and retain water, as discussed in Chapter 1. Additionally, it can also act as a “2way bridge” between soil structure and microbial activity, considering a strong correlation between them (Cui and Holden 2014).

Soil pH is another factor. It is known to influence at least some kinds of bacteria in soil, and has been suggested to be a predictor of bacterial diversity in soil (Feng et al. 2014).

A positive correlation between pH and microbial biomass (a form of OC) exists, and may drive soil OC input (Pietri and Brookes 2008). Soil pH has a strong influence on the bacterial 79

community composition, a somewhat weaker influence on fungal communities (Rousk et al.

2010), and thus may be an influence on the quantity and structure of OM in soil and, therefore, an indirect influence on AWC. Many studies have demonstrated the efficacy of OM to increase AWC in soil through individual field studies as well as meta-analysis (Hudson 1994, Franzluebbers 2002, Wall and Heiskanen 2003, Manns and Berg 2014, Mays et al. 2015). Hudson (1994) analyzed the published data and demonstrated a highly significant correlation between OM and AWC when the soil properties other than OM content were controlled. Although water retention increased for both FC and PWP with increasing OM content, the rate of increase for FC was greater than that of PWP, with the result that as OM increased from 0.5% to 3.0%, the AWC more than doubled (Hudson 1994).

It is widely considered that water demand management will become increasingly important, and fresh water supply is projected to be an issue of serious global concern in the future (IPCC 2014a, Cisneros et al. 2014). As of 2005, at least 35 percent of the global population live in regions with chronic water shortage (Kummu et al. 2010). As of 2016, about two-thirds of the global population (4.0 billion people) live with severe water scarcity at least one month of the year (Mekonnen and Hoekstra 2016). The global population exposure to water shortages is expected to increase through the 21st century (IPCC 2014a, Gosling and Arnell 2016). Problems with water involve either deficiency or excess. By 2100, climate change will impact the frequency and/or magnitude of both droughts (either meteorological droughts as less rainfall or agricultural droughts as drier soil) as well as the risk of flooding in many parts of the globe, including Western Europe, North America, and many other parts of the world (IPCC 2014b, Cisneros et al. 2014).

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Soil degradation decreases soil water holding capacity (Feddema 1998), a condition that could amplify the effects of climate change as well as impact food security (Stocking et al. 2005). Therefore, it is of critical importance to understand the relationship of OM with soil water capacity.

The primary objectives of this study are to conduct a meta-analysis to determine if OM and AWC are significantly correlated in some soils of Ohio, and to develop a model to explain the relationship. The study included 36 counties in the northern region of the state, covering 3 Major Land Resource Areas (Table 4).

Two hypotheses are proposed: 1)

OM is the best individual predictor of AWC.

2)

A multi-variate model is more descriptive of the OM-AWC relationship than a onevariable model.

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Table 4. Ohio counties included in the study, with the Major Land Resource Area (MLRA) designation that the majority of the county is included in.

Code OH003 OH007 OH011 OH035 OH039 OH041 OH043 OH051 OH055 OH063 OH065 OH069 OH085 OH091 OH093 OH095 OH099 OH101 OH103 OH107 OH123 OH125 OH133 OH137 OH143 OH147 OH149 OH151 OH153 OH155 OH159 OH161 OH169 OH171 OH173 OH175

County Allen Ashtabula Auglaize Cuyahoga Defiance Delaware Erie Fulton Geauga Hancock Hardin Henry Lake Logan Lorain Lucas Mahoning Marion Medina Mercer Ottawa Paulding Portage Putnam Sandusky Seneca Shelby Stark Summit Trumbull Union Van Wert Wayne Williams Wood Wyandot

Majority MLRA 111B 139 111B 139 99 111B 139 99 139 111B 111B 99 139 111B 139 99 139 111B 139 111B 99 99 139 99 99 111B 111B 139 139 139 111B 111B 139 111B 99 111B

Majority MLRA Description Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Erie-Huron Lake Plain Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Erie-Huron Lake Plain Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Indiana and Ohio Till Plain, Northeastern Part Erie-Huron Lake Plain Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Erie-Huron Lake Plain Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Erie-Huron Lake Plain Erie-Huron Lake Plain Lake Erie Glaciated Plateau Erie-Huron Lake Plain Erie-Huron Lake Plain Indiana and Ohio Till Plain, Northeastern Part Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Lake Erie Glaciated Plateau Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Indiana and Ohio Till Plain, Northeastern Part Lake Erie Glaciated Plateau Indiana and Ohio Till Plain, Northeastern Part Erie-Huron Lake Plain Indiana and Ohio Till Plain, Northeastern Part

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5.3

Methods The USDA defines Major Land Resource Areas (MLRA) to identify regions with

similar characteristics, such as soil, climate, and land use (USDA-NRCS 2005). Initially, two MLRAs were chosen for this study (Figure 13). MLRA 99 (Erie-Huron Lake Plain) is 28,370 km2 (10,950 mi2) and includes parts of Michigan, Indiana, and Ohio (USDA-NRCS 2006a).

MLRA 139 (Lake Erie Glaciated Plateau) is 27,770 km 2 (10,715 mi2) and includes parts of Ohio, Pennsylvania, and New York (USDA-NRCS 2006b). Later, a third MLRA was included. MLRA 111B (Indiana and Ohio Till Plain, Northeastern Part) is 34,880 km 2 (13,460 mi2) and includes parts of Michigan, Indiana, and Ohio (USDA-NRCS 2006c).

Soil survey data were downloaded from the Web Soil

Figure 13. Major land resource areas (MLRA) chosen for this study. Blue = MLRA 99, red = MLRA 111B, yellow = MLRA 139.

Survey (WSS), an online database maintained by the Natural Resources Conservation Service, for Ohio counties in MLRA 99 and MLRA 139 initially, listed in Table 4 (22 total). Given that the relationship between OM and AWC is dependent upon many factors, several additional soil and site parameters were extracted, including soil texture,

Background map credit: Google Earth

sample depth, clay content, Db, pH, slope, FC, and PWP. 83

The WSS data were provided by county in individual Microsoft Access databases, and custom queries were scripted to extract the data for this study from each of the databases. The extracted data were compiled into an Excel spreadsheet. The consistent formatting of the databases provided seamless compilation of the data.

As soil properties from the WSS data (clay, D b, pH, slope, texture class, and depth) were initially analyzed and filtered to consistent values in order to remove their influence (as will be discussed later), it was discovered that all remaining records within the two primary fields of interest, OM and AWC, had exactly the same values (Figure 14). This seemed unlikely and nonrandom and it was learned that all data available from the WSS are representative values of collections of samples (Paul Finnell, personal communication, 11/14/2016), and not the raw data themselves.

At this point, a second source of data was investigated which did supply actual soil sample data â&#x20AC;&#x201C; the National Cooperative Soil Survey Soil Characterization Data (NCSS), also an online source. This dataset provided a substantial challenge to process for computational Figure 14. Descriptive statistics for soil organic matter (OM) and available water capacity (AWC) after filtering Web Soil Survey data to prepare for modeling.

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analysis. Although data could also be downloaded by county, it was grouped into several different spreadsheets, the data from which would need to be joined together. It was also discovered that the same parameter (such as D b) could be reported in multiple spreadsheets.

Additionally, due to the long-term nature of the data collection program, collection and analysis methods changed and expanded over time, resulting in multiple possible columns for a given soil parameter. For example, D b was reported as field moist, FC moist (33 kPa), or oven-dry; in any one of 17 differently-named columns; included – or not included – in any one of three different spreadsheets; and placed in different column positions within the same spreadsheet results provided by different counties. Also, there was not a straightforward “key” value provided to join the soil parameters to related site information, which included other variables of interest such as soil texture and slope.

Although several thousand soil samples were available from the NCSS for the same counties as provided by the WSS, there were fewer than 500 total samples reporting any D b data. After filtering was applied to restrict samples to consistent D b, clay, and pH, there were not enough records remaining with which to conduct this study. Consequently, counties from an additional MLRA in Ohio (111B) were downloaded and the filtering stringency reduced in both sets in order to provide a sufficient quantity of appropriate data for the modeling effort. The parameter values for filtering were chosen based on the highest number of available records after various filters were applied.

The MATLAB mathematical programming software (MATLAB 2016) was chosen as the computational tool to process the data and perform the modeling for this study. Built-in MATLAB functions used in the study, and noted in this text, are formatted in italics.

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Importing the WSS data was a simple xlsread command and a matrix formed from the specific fields of interest. The NCSS data, however, required a substantial amount of massaging. Preliminary scrubbing of data on both sets included removing rows where all fields of interest were blank, rows where there wasnâ&#x20AC;&#x2122;t at least one form of water data (FC, PWP, or AWC), and rows that did not have a measurement for each of OM, Db, and clay.

Table 5. Results of individual predictors of available water content for (a) the Web Soil Survey data and (b) National Cooperative Soil Survey (NCSS) data. (Note: Slope was not included in the NCSS data.) (a) Parameter Bulk Density Organic Matter pH Clay Texture Class Slope

R2 0.38 0.22 0.16 0.12 0.03 0.0001

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.4

(b) Parameter Bulk Density Clay Organic Matter pH Texture Class

R2 0.12 0.05 0.03 0.03 0.02

< 0.001 < 0.001 0.013 0.014 0.056

p

p

Rank 1 2 3 4 5 6 Rank 1 2 3 4 5

To begin the analysis of the hypotheses, each dataset was first restricted to mineral soils by eliminating any record with OM content of <= 12%, or the equivalent OC content of <=7%. Additionally, records >7.0 pH were eliminated, to reduce the chance of inclusion of those samples with inorganic C in the data, which can precipitate as calcium carbonate in soils with a pH greater than 7.0.

All remaining records were used unfiltered for the hypothesis-testing phase of this study.

To test the first hypothesis, a correlation matrix was created through pairwise fit (corrcoef) and the results ranked (tabulate) in descending order of the absolute value of the correlation coefficient (R) value (MATLAB 2016).

86

To test the second hypothesis, multi-variate linear regression was used (regress) with the significantly-correlated parameters identified in the second hypothesis. Each variable was combined with the other variables in every possible combination, as well as models that also included the combined effects of variables, for those with multiple variables. This resulted in 22 total models to compare.

After verifying that the residuals of each of the 22 regressions did not reject the null hypothesis for normality (ttest), they were each validated with Akaike's Information Criterion for each model and sorted in ascending order to determine the model that best explained the relationship with AWC. Before modeling the relationship specifically between OM and AWC, the data were filtered to remove the influence of other factors, which the second hypothesis confirmed. As explained earlier, restricting the other influential parameters resulted in either unusable (due to data summarization in the WSS dataset) or too few (in the NCSS dataset) records.

Therefore, the filtering stringency was modified to allow a small range of values among the most influential factors: Db, clay, and pH. To fit models to the OM-AWC relationship, several models were tested: 

Linear (regress)



Polynomial (fit, ‘poly2’ parameter)



Exponential (fit, ‘exp1’ parameter)

The residuals were tested for normality (ttest) and output from each model was correlated to the sample data (corr) to determine significance (p value).

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5.4

Results and Discussion The MLRAs used in this study â&#x20AC;&#x201C; 99, 111B, and 139 â&#x20AC;&#x201C; include 9, 14, and 13 counties

in Ohio, respectively, for a total of 36. These counties collectively included 21,337 and 6,189 total records from the WSS and NCSS datasets, respectively. The WSS dataset included over

Figure 15. Descriptive statistics for soil organic matter (OM), available water capacity (AWC), field capacity (FC), and permanent wilting point (PWP) before filtering for modeling, Web Soil Survey data.

88

three times the number of records as the NCSS dataset, despite each record being a composite of a collection of samples.

The data from both sources include samples collected over time since at least the 1950â&#x20AC;&#x2122;s. There is spatial and temporal variability in collection and analytical methods

Figure 16. Descriptive statistics for soil organic matter (OM), available water capacity (AWC), field capacity (FC), and permanent wilting point (PWP) before filtering for modeling, National Cooperative Soil Survey data.

89

employed, which have not been accounted for, or possibly needing adjustment for, in this study.

Preliminary scrubbing and initial filtering of data to mineral soils and <=7.0 pH resulted in 20,506 and 254 records from the WSS and NCSS datasets, respectively. These subsets were used for the first hypothesis testing.

Figure 15 and Figure 16 provide visual distributions of each of the four OM and water-based fields from the WSS and NCSS datasets, respectively. The normal probability plots indicate a reasonable distribution of data for each field. The WSS box plots indicate many outliers, particularly at low FC contents (Figure 15). Correspondingly, the majority of the WSS OM data are also low, with a median of 0.65% (Figure 15) and mean of 1.17% OM. The NCSS data also indicate low OM levels, with a median of 1.10% (Figure 16) and mean of 1.94% OM. However, the data provided by the WSS and NCSS have been compiled from a variety of sources over time. It is conceivable that the low average OM of each database could be due to preferential soil sampling of farms experiencing productivity declines resulting from low OM and other related problems, possibly resulting in bias, though the motives for each sampling are unknown.

There is disagreement regarding setting a particular OC or OM content as a threshold indicator of degraded soil. Soils with less than 2% OM are associated with structural instability (Kemper and Koch 1966). Loveland and Webb (2003) concluded that using a generally-accepted 2% OC (4% OM) threshold for temperate-zone soils was inconclusive as a standard indicator of degraded soil, although they found evidence that 1% OC (2% OM) could be a threshold for crop productivity (Aune and Lal 1997), one of several indicators of degradation. Despite a lack of consensus on a definition of â&#x20AC;&#x153;degradedâ&#x20AC;? soil in 90

the context of OM, it is clear from the low OM contents in these datasets that many soils in northern Ohio are under threat of losing productivity and soil structure. This has implications for the regionâ&#x20AC;&#x2122;s economy as well as food security.

Table 5 lists the results of the first hypothesis, which predicted that OM is the best individual predictor of AWC. Both datasets indicate that D b is the best individual predictor in the presence of other factors potentially exerting an influence, rejecting the hypothesis.

This is not a surprise, as the definition of D b is the ratio of the mass of a substance to its volume. In soil, the density defines how much pore space is present, which is what holds the water and therefore defines the capacity of water that soil can hold. The WSS dataset ranked OM as the second-best predictor after Db, which means OM is the best predictor of those variables that donâ&#x20AC;&#x2122;t have the potential capacity of water as part of its definition. Additionally, OM itself affects Db, with which it has a negative correlation (Arvidsson 1998, Augustin and Cihacek 2016). The NCSS ranked OM as the third-best predictor, after Db and clay.

Figure 17. Percent of variance explained by each factor in the Principle Component Analysis (PCA) for the NCSS and WSS data sets. White numbers in parenthesis are the eigenvalues for each factor.

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Without OM, water retention is a function of pore space and mineral surface area (Olness and Archer 2005). Therefore, where OC is <2% (<4% OM), clay and Db are more influential on AWC (Manns and Berg 2014). Considering the median OM content of each dataset, the NCSS

Table 6. Coefficients (loadings) on principal components (PC).

data concurred strongly with these findings, the WSS data

Db C Clay pH

PC1 -0.60 0.64 0.41 0.26

NCSS PC2 PC3 0.43 0.01 -0.19 -0.29 0.43 0.80 0.77 -0.52

PC4 0.68 0.69 0.08 -0.25

PC1 0.64 -0.42 0.47 0.44

WSS PC2 PC3 0.04 -0.06 0.75 0.37 0.65 -0.37 -0.04 0.85

PC4 0.76 0.34 -0.46 -0.30

somewhat less so.

In addition to correlation testing, Principle Component (PC) Analysis was also employed to investigate the relative importance of these four parameters. Due to differing units among the attributes, the data were first standardized by converting each value to its zscore, then performing the analysis (pca).

Figure 18. Scores from the first two principal components of the a) NCSS and b) WSS data sets plotted together with the relative influences of each soil attribute on the two factors. (Due to a software bug in Matlab, the clay line is obscured in b). a) b)

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The NCSS data set resulted in 2 PCs with eigenvalues > 1 which together explained 74.8% of the variability of the data (Figure 17). An eigenvalue < 1 indicates that the PC factor explains the variability no better than an individual attribute and is sometimes excluded (Sharma 1996). However, since the third PC can account for an additional 18.6%, for a total of 93.4% of the data explained, it was included in this analysis. In the NCSS data set, C and Db had the greatest influence on PC1 and show a strong negative correlation, while pH had the greatest influence on PC2, followed by equal contributions from Db and clay. PC3 is dominated by clay with some influence from a negative pH loading. A biplot (biplot) of the three PCs (Figure 18a) indicates that all four factors are similar in influence, Table 7. Top three and last three results of multi-variate model testing for (a) Web Soil Survey data and (b) National Cooperative Soil Survey data to determine which best describes AWC. (a) Rank 1 2 3 • 20 21 22

AWC Model OM+Clay+Bulk Density OM+Clay+Bulk Density+pH Clay+Bulk Density+pH • pH Clay OM

(b) Rank 1 2 3 • 20 21 22

AWC Model Clay+Bulk Density+pH+(I) OM+Clay+Bulk Density+pH+(I) OM+Bulk Density+(I) • Bulk Density+pH+(I) OM pH

AIC Score 101714 102602 103281 • 109034 109968 110981

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AIC Score 1401 1402 1407 • 1452 1458 1459

R2 0.34 0.36 0.36 • 0.16 0.12 0.08 R2 0.23 0.26 0.13 • 0.15 0.03 0.03

p <0.001 <0.001 <0.001 • <0.001 <0.001 <0.001

p <0.001 <0.001 <0.001 • <0.001 0.01 0.01

and confirms the negative correlation between Db and C, and the strong influence of clay in this data set.

The WSS data set resulted in only 1 PC (Table 6) with an eigenvalue > 1, explaining only 51.6% of the variability. Since PC2 and PC3 explained 21.9% and 20.2%, respectively, they were included in the analysis, for a total of 93.7% of the data explained. In PC1, D b has Figure 19. Modeling soil organic matter (OM) to available water capacity (AWC) from the Web Soil Survey data with a linear (black line) and polynomial (blue line) models.

the dominant influence with the remaining 3 variables having a nearly equal influence. PC2 is dominated

by C and clay, while PC3 is dominated by pH with a smaller negative correlation between C and clay. Figure 18b also shows a negative correlation between Db and C, though weaker than the

NCSS data set results. In the WSS data, pH is a more prominent influence as compared to the NCSS data set.

Since there were only four soil parameters that were significant as individual predictors of AWC between both datasets (D b, OM, clay, and pH), the second hypothesis testing was limited to those parameters. The second hypothesis expected that a multi-variate model would provide a better explanation of the variability in AWC than a single variable alone, as explored in the first hypothesis testing. Table 7 lists the results of the top three and 94

bottom three results. The results confirm this hypothesis, as analysis on both datasets resulted in the top three models including three or four variables, some also including their interactions. For comparison, five of the six total bottom-rated models consisted of a single variable. All six of the top models included D b, clay was included in five of the total top six, OM in four of the six, and pH in two. This is also consistent with the intuitive finding that clay and Db are more influential on AWC than OM (Manns and Berg 2014) at low OM content.

To model the relationship, filtering was applied to the datasets to remove the influential effects of these other variables, which is necessary in order to isolate

Figure 20. Modeling soil organic matter (OM) to available water capacity (AWC) from the National Cooperative Soil Survey data with a linear (black line) and polynomial (blue line) models.

the relationship between OM and AWC (Hudson 1994).

Both a linear and a polynomial model were fitted to the data, with the polynomial model resulting in a significant fit on the WSS data (Figure 19), and neither model significant on the NCSS data (Figure 20). Exponential models were also attempted (not shown) and resulted in lines almost identical to the linear models.

Results are influenced by the summarized nature of the WSS dataset and the skewed distribution resulting from the filtering, which resulted in 284 records. As previously 95

explained, each WSS â&#x20AC;&#x153;sampleâ&#x20AC;? is a composite with a single representative value for the collection of samples it represents. These composites can have a large variability in the ranges of values they represent. For example, the OM content for each mineral soil record in the Ashtabula county data at the 0 to 20 cm depth ranged from a 2% OM content difference between the high and low values for a given record (such as a 1% OM low value and a 3% OM high value) to a 5.5% OM content difference. Figure 21 illustrates the clustering of values this resulted in, as well as the dearth of records representing OM content higher than 3%.

The filtered NCSS data has a more expected distribution and is, in fact normal (not shown), but is limited to only nine points. As explained previously, the data filtering necessarily resulted in some variability in parameters that should have been held constant (Table 8). This was unavoidable under the circumstances, but consequently introduced a degree of uncertainty into the results. There was a wider range of clay values in the NCSS

Figure 21. Descriptive statistics for soil organic matter (OM) and available water capacity (AWC) with adjusted filtering Web Soil Survey data to prepare for modeling.

96

data (Table 8), though clay was a stronger individual predictor of AWC than OM, and OMâ&#x20AC;&#x2122;s individual correlation with AWC was not even significant in the NCSS data (Table 5). Clay is known to have a positive correlation with water content (Mungare et al. 1983, Katterer et al.

2006), consequently, it is reasonable to assume that clay exerted an influence on the results.

The filtered records disproportionately represented MLRA 139 so the modeling results realistically apply primarily to the MLRA 139 region of Ohio, and not over the three MLRA regions chosen for this study in general, nor should the results be extrapolated to all of Ohio. This is

Table 8. Soil and site parameters with their percent representation in the final selection of records for each dataset: Web Soil Survey (WSS) and National Cooperative Soil Survey (NCSS). (Note: Slope was not included in the NCSS data.) Bold parameters delineate those which were identified as having a significant relationship with Available Water Capacity and would have been restricted to a constant value, had the nature and scope of the available data allowed it.

Parameter Bulk density Bulk density Bulk density Clay Clay Clay Clay Clay Depth Depth MLRA MLRA MLRA Organic Matter pH pH pH Other pH Other pH Slope Slope Other Slope Texture

Value 1.46 g/cm3 1.47 g/cm3 1.5* g/cm3 20% 21% 25%* 26%* 27%* 0 to 20 cm 1 to 33 cm 99 111B 139

< 12% 4.6 4.7 6.3 4.5 to 6.5 4.4 to 5.9 1% 4% 0.2 to 55% Silt Loam Silty Clay Texture Loam Texture Loam * Indicates rounded values

not totally unexpected, given the

97

WSS 42% 58%

NCSS 100%

51% 49% 45% 33% 22% 100% 2% 6% 92% 100% 36%

100% 41% 59% 100% 22% 22%

41% 23% 29% 38% 33% 96% 2% 2%

56% N/A N/A N/A 88% 12%

definition of the MLRAs, which will naturally contain samples with similar soil properties.

The grouping(s) with the highest number of available samples happened to be chosen.

In a brief scrutiny of OM to FC and PWP, as they define the range of AWC at any given OM content, linear and polynomial models were fitted (top half and bottom half of Figure 22, respectively) to the WSS data. The linear models exhibit a reasonably good fit. The polynomial

Figure 22. Modeling organic matter (OM) to both field capacity (FC) and permanent wilting point (PWP), Web Soil Survey data. Upper half is FC, lower half is PWP. Black lines are linear models, blue lines are polynomial, and dashed red are theoretical logarithmic relationships, added for comparison.

models show only a slightly better fit for FC, and no difference for PWP. All four models are significant.

A visual inspection suggests that, as OM increases, the rate of increase of AWC begins to decrease at about 3.5% OM, as compared to the linear model. The rate of increase of the PWP shows neither signs of increase nor decrease, as both models are effectively linear. Theoretical logarithmic curves (dashed red lines on Figure 22) were also overlaid for comparison as the expected result. The residuals of the logarithmic curves were not normal, so the fit and significance of the lines are not applicable. However, they illustrate an alternative possibility hinted at by the survey data, and as found by others (Wall and Heiskanen 2003).

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5.5

Conclusion According to the analysis conducted in this study, the first hypothesis failed, as D b

was ranked as the best individual predictor of the soil properties compared in this study.

However, if Db is disregarded (given its definition implies pore space, and therefore water capacity), then OM and clay were ranked first the WSS and NCSS data sets, respectively. The second hypothesis was not disproven, as the models with the highest correlation tended to include the most variables.

Although OM was not found to be the highest individual predictor of AWC, there was a significant correlation found in both data sets. In general, the best models contained OM as a variable, though clay occurred with higher frequency in the models with the highest correlation.

The models in this study indicate that soils over the study areas will have their maximum water capacity at approximately 3% OM content. However, due to the sparseness of data between 3% and 7% OM, as well as issues with the data already discussed, there is considerable uncertainty with this particular finding.

The results of the hypotheses tested in this study could have been influenced by: ď&#x201A;ˇ

Collection and analytical methods that have changed over time, possibly resulting in a need to apply mathematical corrections to values, though this need was not verified.

ď&#x201A;ˇ

The OM levels in the regions selected, as the data do concur with other findings regarding the relative influences of soil properties at low OM content.

99



Considerable limitations of the datasets resulting in heavy clustering of values, or a simple lack of data.



Filtering choices which allowed a possible degree of variability from other properties with an influence on AWC.

Future possible research could include: 

Generating spatial estimation maps of OC/OM over the MRLAs in the study (or even the entire state), possibly multiple maps representing different time periods, to see the changes in OC/OM over time.



5.6

Field validation of the model should also be conducted.

Acknowledgements Gratitude is extended to Dr. Gil Bohrer and Dr. Paula Mouser for their guidance on

the development of this chapter and review of its first draft.

100

5.7

References

Arvidsson, Johan. “Influence of Soil Texture and Organic Matter Content on Bulk Density, Air Content, Compression Index and Crop Yield in Field and Laboratory Compression Experiments.” Soil and Tillage Research 49, no. 1–2 (November 17, 1998): 159–70. doi:10.1016/S0167-1987(98)00164-0.

Augustin, Christopher, and Larry J. Cihacek. “Relationships Between Soil Carbon and Soil Texture in the Northern Great Plains:” Soil Science 181, no. 8 (August 2016): 386–92. doi:10.1097/SS.0000000000000173.

Aune, J. and R. Lal 1997. Agricultural productivity in the tropics and critical limits of properties of Oxisols, Ultisols and Alfisols. Trop. Agric. (Trinidad) 74:96-103.

Barker, David, Jim Beuerlein, Anne Dorrance, Don Eckert, Bruce Eisley, Ron Hammond, Edward Lentz, et al. Ohio Agronomy Guide. 14th ed. Bulletin 472–05. Columbus, Ohio: Ohio State University Extension, 2005.

Blanco-Canqui, Humberto, and Joseph G. Benjamin. “Impacts of Soil Organic Carbon on Soil Physical Behavior.” In Advances in Agricultural Systems Modeling, edited by Sally Logsdon, Markus Berli, and Rainer Horn. Soil Science Society of America, Inc., 2013. https://dl.sciencesocieties.org/publications/books/abstracts/advancesinagric/quantify ingandm/11.

Blanco-Canqui, H., and R. Lal. “Mechanisms of Carbon Sequestration in Soil Aggregates.”

Critical Reviews in Plant Sciences 23, no. 6 (2004): 481–504. doi:10.1080/07352680490886842.

101

Boivin, P., B. Schäffer, and W. Sturny. 2009. Quantifying the relationship between soil organic carbon and soil physical properties using shrinkage modelling. Eur. J. Soil Sci. 60:265–275. doi:10.1111/j.1365-2389.2008.01107.x.

Cisneros J.B.E., T. Oki, N.W. Arnell, G. Benito, J.G. Cogley, P. Döll, T. Jiang, and S.S. Mwakalila, 2014: Freshwater resources. In: Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229-269.

Combs, S.M., and M.V. Nathan. 1998. Ch. 12: Soil Organic Matter. In Recommended Chemical Soil Test Procedures for the North Central Region. Missouri Agricultural Experiment Station Bulletin 1001.

Deurer, Markus, Siva Sivakumaran, Stefanie Ralle, Iris Vogeler, Ian Mclvor, Brent Clothier, Steve Green, and Joerg Bachmann. “A New Method to Quantify the Impact of Soil Carbon Management on Biophysical Soil Properties: The Example of Two Apple Orchard Systems in New Zealand.” Journal of Environmental Quality 37, no. 3 (June 2008): 915–24. doi:10.2134/jeq2007.0508.

Feng, Youzhi, Paul Grogan, J. Gregory Caporaso, Huayong Zhang, Xiangui Lin, Rob Knight, and Haiyan Chu. “pH Is a Good Predictor of the Distribution of Anoxygenic Purple Phototrophic Bacteria in Arctic Soils.” Soil Biology and Biochemistry 74 (July 2014): 193–200. doi:10.1016/j.soilbio.2014.03.014.

102

Franzluebbers, A. J. “Water Infiltration and Soil Structure Related to Organic Matter and Its Stratification with Depth.” Soil & Tillage Research 66, no. 2 (July 2002): 197–205. doi:10.1016/S0167-1987(02)00027-2.

Gomiero, Tiziano. “Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge.” Sustainability 8, no. 3 (March 2016). doi:10.3390/su8030281.

Gosling, Simon N., and Nigel W. Arnell. “A Global Assessment of the Impact of Climate Change on Water Scarcity.” Climatic Change 134, no. 3 (February 2016): 371–85. doi:10.1007/s10584-013-0853-x.

Hudson, Berman D. “Soil Organic Matter and Available Water Capacity.” Journal of Soil and Water Conservation 49, no. 2 (1994): 189+.

IPCC, 2014a: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.

IPCC, 2014b: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Kätterer, T., O. Andrén, and P-E. Jansson. “Pedotransfer Functions for Estimating Plant Available Water and Bulk Density in Swedish Agricultural Soils.” Acta Agriculturae 103

Scandinavica, Section B - Plant Soil Science 56, no. 4 (December 2006): 263–76. doi:10.1080/09064710500310170.

Kemper, William Doral, Elbry James Koch, United States, and Department of Agriculture.

Aggregate Stability of Soils from Western United States and Canada; Measurement Procedure, Correlations with Soil Constituents. Washington: Agricultural Research Service, U.S. Dept. of Agriculture; [for sale by the Supt. of Docs., U.S. Govt. Print.

Off., 1966.

Kummu, Matti, Philip J. Ward, Hans de Moel, and Olli Varis. “Is Physical Water Scarcity a New Phenomenon? Global Assessment of Water Shortage over the Last Two Millennia.” Environmental Research Letters 5, no. 3 (September 2010): 34006. doi:10.1088/1748-9326/5/3/034006.

Lal, R. 2014.Societal value of soil carbon.J.Soil Water Conserv.69:186A-192A. Lal, Rattan and Manoj Shukla. Principles of Soil Physics. New York, NY: Marcel Dekker, Inc., 2004.

Lal.R., K.Lorenz, R.R.K. Huttle, B.U. Schneider, and J.Von Braun( Eds) 2013.Ecosystem Services and Carbon Sequestration in the Biosphere. Springer, Dordrecht, Netherland, 464 pp.

Loveland, P, and J Webb. “Is There a Critical Level of Organic Matter in the Agricultural Soils of Temperate Regions: A Review.” Soil and Tillage Research 70, no. 1 (March 2003): 1–18. doi:10.1016/S0167-1987(02)00139-3.

Manns, Hida R., and Aaron A. Berg. “Importance of Soil Organic Carbon on Surface Soil Water Content Variability among Agricultural Fields.” Journal of Hydrology,

104

Determination of soil moisture: Measurements and theoretical approaches, 516 (August 4, 2014): 297–303. doi:10.1016/j.jhydrol.2013.11.018.

MATLAB. 2016. Version 9.0.0.341460(R2016a). The MathWorks, Inc., Natick, Massachusetts, United States.

Mays, Neal, Curt Richard Rom, Kristofor R. Brye, Mary C. Savin, and M. Elena Garcia.

“Groundcover Management System and Nutrient Source Impacts on Soil Quality Indicators in an Organically Managed Apple (Malus X Domestica Borkh.) Orchard in the Ozark Highlands.” Hortscience 50, no. 2 (February 2015): 295–303.

Mekonnen, M. M., and A. Y. Hoekstra. “Four Billion People Facing Severe Water Scarcity.”

Science Advances 2, no. 2 (February 12, 2016): e1500323–e1500323. doi:10.1126/sciadv.1500323.

Mungare, T.S., A.K. Shingte, and K.S. Pharande. “Relationship of Available Water Capacity with Some of the Physical Properties of Soils.” Journal of Maharashtra Agricultural Universities 8, no. 1 (1983): 9–13.

Olness, A., and D. Archer. “Effect of Organic Carbon on Available Water in Soil.” Soil Science 170, no. 2 (February 2005): 90–101. doi:10.1097/01.ss.0000155496.63323.35.

Pietri, J. C. A., and P. C. Brookes. “Relationships between Soil pH and Microbial Properties in a UK Arable Soil.” Soil Biology and Biochemistry 40, no. 7 (July 2008): 1856–61. doi:10.1016/j.soilbio.2008.03.020.

Pribyl, Douglas W. “A Critical Review of the Conventional SOC to SOM Conversion Factor.” Geoderma 156, no. 3–4 (May 15, 2010): 75–83. doi:10.1016/j.geoderma.2010.02.003.

105

Rajan, K., A. Natarajan, K. S. Anil Kumar, M. S. Badrinath, and R. C. Gowda. “Soil Organic Carbon - the Most Reliable Indicator for Monitoring Land Degradation by Soil Erosion.” Current Science (00113891) 99, no. 6 (September 25, 2010): 823–27.

Rawls, W. J., Y. A. Pachepsky, J. C. Ritchie, T. M. Sobecki, and H. Bloodworth. “Effect of Soil Organic Carbon on Soil Water Retention.” Geoderma, Quantifying agricultural management effects on soil properties and processes, 116, no. 1–2 (September 2003): 61–76.

Rousk, Johannes, Erland Bååth, Philip C. Brookes, Christian L. Lauber, Catherine Lozupone, J. Gregory Caporaso, Rob Knight, and Noah Fierer. “Soil Bacterial and Fungal Communities across a pH Gradient in an Arable Soil.” The ISME Journal 4, no.

10 (October 2010): 1340–51. doi:10.1038/ismej.2010.58.

Stocking, A., F. H. Obando, and A. Tengberg. “Soil Degradation and Its Impact on Plants in Drylands.” In Sustainable Use and Management of Soils - Arid and Semiarid Regions, edited by A. F. Cano, R. O. Silla, and A. R. Mermut, 36:355–68, 2005.

USDA-NRCS. 2005. MLRA Definitions | NRCS Soils. December 15, 2005. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2 _053625 USDA-NRCS. 2006a. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: L - Lake State Fruit, Truck Crop, and Dairy Region; 99 - Erie-Huron Lake Plain. Major Land Resource Regions Custom Report, 2006. http://soils.usda.gov/MRLAExplorer.

USDA-NRCS. 2006b. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: R - Northeastern Forage and Forest 106

Region; 139 - Lake Erie Glaciated Plateau. Major Land Resource Regions Custom Report, 2006. http://soils.usda.gov/MRLAExplorer.

USDA-NRCS. 2006c. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: M – Central Feed Grains and Livestock Region 111B – Indiana and Ohio Till Plain, Northeastern Part. Major Land Resource Regions Custom Report, 2006. http://soils.usda.gov/MRLAExplorer.

Wall, A., and J. Heiskanen. “Water-Retention Characteristics and Related Physical Properties of Soil on Afforested Agricultural Land in Finland.” Forest Ecology and Management 186, no. 1–3 (December 5, 2003): 21–32. doi:10.1016/S0378-1127(03)00239-1.

Zhuang, J., J.F. McCarthy, E. Perfect, L.M. Mayer, and J.D. Jastrow. 2008. Soil water hysteresis in water-stable microaggregates as affected by organic matter. Soil Sci. Soc. Am. J. 72:212–220. doi:10.2136/sssaj2007.0001S6.

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Chapter 4: Conclusion While evidence of a significant correlation between OM and AWC was found in northern Ohio, it is not a strong correlation, though there is uncertainty due to the nature of the data sets employed. However, a simple examination of the raw data suggests that many soils in northern Ohio are under threat of losing productivity and soil structure because OM levels are already below optimum in sections of the area, and are at risk of accelerated decomposition due to climate change which will consequently reduce their capacity to hold water - and provide other critical soil functions - even further. This has implications for the region’s economy as well as food security. The ability of soil to absorb and retain water through adequate OM has important global significance. Water is a vital resource for all species, and human land management decisions have a direct influence on the availability and quality of this resource. Future management practices will need to protect the soil surface from heat and sustain – or increase – C inputs.

Future research ideas: 

Expand SOM-AWC correlation studies to full-MLRA analysis, such as MLRA 139 and possibly MLRA 99, including counties outside Ohio.



Re-run existing analyses, should a more robust data source be available.



Incorporate a microbial component to carbon models and carbon components of earth system models to capture effects of eCO2 and the priming effect.

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Complete Reference List Abebe, A., H. Pathak, S.D. Singh, A. Bhatia, R.C. Harit, and V. Kumar. 2016. Growth, Yield and Quality of Maize with Elevated Atmospheric Carbon Dioxide and Temperature in North–west India. Agriculture, Ecosystems & Environment 218 (February 15, 2016): 66–72. Ågren, Göran I., and J. Å. Martin Wetterstedt. 2007. What Determines the Temperature Response of Soil Organic Matter Decomposition? Soil Biology and Biochemistry 39, no. 7 (July 2007): 1794–98. doi:10.1016/j.soilbio.2007.02.007.

Angers, D. A., and N. S. Eriksen-Hamel. 2008. Full-Inversion Tillage and Organic Carbon Distribution in Soil Profiles: A Meta-Analysis. Soil Science Society of America Journal 72, no. 5 (October 2008): 1370–74.

Arvidsson, Johan. Influence of Soil Texture and Organic Matter Content on Bulk Density, Air Content, Compression Index and Crop Yield in Field and Laboratory Compression Experiments. Soil and Tillage Research 49, no. 1–2 (November 17, 1998): 159–70. doi:10.1016/S0167-1987(98)00164-0.

Attavanich, W., and B.A. McCarl. 2014. How Is CO2 Affecting Yields and Technological Progress? A Statistical Analysis. Climatic Change 124, no. 4 (June 2014): 747–62.

Augustin, Christopher, and Larry J. Cihacek. Relationships Between Soil Carbon and Soil Texture in the Northern Great Plains. Soil Science 181, no. 8 (August 2016): 386–92. doi:10.1097/SS.0000000000000173.

109

Aune, J. and R. Lal 1997. Agricultural productivity in the tropics and critical limits of properties of Oxisols, Ultisols and Alfisols. Trop. Agric. (Trinidad) 74:96-103.

Averill, Colin, Bonnie G. Waring, and Christine V. Hawkes. 2016. Historical Precipitation Predictably Alters the Shape and Magnitude of Microbial Functional Response to Soil Moisture. Global Change Biology 22, no. 5 (May 1, 2016): 1957–64. doi:10.1111/gcb.13219.

Barker, David, Jim Beuerlein, Anne Dorrance, Don Eckert, Bruce Eisley, Ron Hammond, Edward Lentz, et al. Ohio Agronomy Guide. 14th ed. Bulletin 472–05. Columbus, Ohio: Ohio State University Extension, 2005.

Basche, A.D., S.V. Archontoulis, T.C. Kaspar, D.B. Jaynes, T.B. Parkin, and F.E. Miguez.

2016. Simulating Long-Term Impacts of Cover Crops and Climate Change on Crop Production and Environmental Outcomes in the Midwestern United States.

Agriculture, Ecosystems & Environment 218 (February 2016): 95–106.

Birch, H. F. 1958. The Effect of Soil Drying on Humus Decomposition and Nitrogen Availability. Plant and Soil 10, no. 1: 9–31. doi:10.1007/BF01343734.

Blanco-Canqui, H. 2011. Does no-till farming induce water repellency to soils? Soil Use Manage. 27:2–9. doi:10.1111/j.1475-2743.2010.00318.x.

Blanco-Canqui, H., and J.G. Benjamin. 2013. Impacts of Soil Organic Carbon on Soil Physical Behavior. In Advances in Agricultural Systems Modeling, edited by Sally Logsdon, Markus Berli, and Rainer Horn. Soil Science Society of America, Inc., 2013. https://dl.sciencesocieties.org/publications/books/abstracts/advancesinagric/quantify ingandm/11.

110

Blanco-Canqui, H., and R. Lal. 2004. Mechanisms of Carbon Sequestration in Soil Aggregates. Critical Reviews in Plant Sciences 23, no. 6 (2004): 481–504. doi:10.1080/07352680490886842.

Blanco-Canqui, H., and R. Lal. 2008. No-Tillage and Soil-Profile Carbon Sequestration: An on-Farm Assessment. Soil Science Society of America Journal 72, no. 3 (June 2008): 693–701.

Boivin, P., B. Schäffer, and W. Sturny. 2009. Quantifying the relationship between soil organic carbon and soil physical properties using shrinkage modelling. Eur. J. Soil Sci. 60:265–275. doi:10.1111/j.1365-2389.2008.01107.x.

Bukovsky, M.S., 2011: Masks for the Bukovsky regionalization of North America, Regional Integrated Sciences Collective, Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO. Accessed 2016-10-03. [http://www.narccap.ucar.edu/contrib/bukovsky/] Carney, K.M., B.A. Hungate, B.G. Drake, and J.P. Megonigal. 2007. Altered Soil Microbial Community at Elevated CO2 Leads to Loss of Soil Carbon. Proceedings of the National Academy of Sciences of the United States of America 104, no. 12 (March 20, 2007): 4990–95.

Cheng, L., F.L. Booker, C. Tu, K.O. Burkey, L. Zhou, H.D. Shew, T.W. Rufty, and S. Hu. 2012. Arbuscular Mycorrhizal Fungi Increase Organic Carbon Decomposition Under Elevated CO2. Science 337 (August 31, 2012): 1084–87.

Cheng, Weixin, William J. Parton, Miquel A. Gonzalez-Meler, Richard Phillips, Shinichi Asao, Gordon G. McNickle, Edward Brzostek, and Julie D. Jastrow. Synthesis and

111

Modeling Perspectives of Rhizosphere Priming. New Phytologist 201, no. 1 (January 2014): 31–44. doi:10.1111/nph.12440.

Chenu, C. and Guerif, J. 1991. Mechanical strength of clay minerals as influenced by an adsorbed polysaccharide. Soil Sci. Soc. Am. J. 55: 1076–1080.

Cisneros J.B.E., T. Oki, N.W. Arnell, G. Benito, J.G. Cogley, P. Döll, T. Jiang, and S.S. Mwakalila, 2014: Freshwater resources. In: Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229-269.

Cole, S., and L. McCarthy. 2015. 2014 Warmest Year in Modern Record. Climate Change: Vital Signs of the Planet, January 16, 2015. http://climate.nasa.gov/news/2221/2014warmest-year-in-modern-record/.

Coleman, K., and D.S. Jenkinson. 2014. RothC - A Model for the Turnover of Carbon in Soil. Harpenden, UK: Rothamsted Research.

Collins, M., R. Knutti, J. Arblaster, J.L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver and M. Wehner.

2013. Long-term Climate Change: Projections, Commitments and Irreversibility. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, 112

Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Combs, S.M., and M.V. Nathan. 1998. Ch. 12: Soil Organic Matter. In Recommended Chemical Soil Test Procedures for the North Central Region. Missouri Agricultural Experiment Station Bulletin 1001.

Conant, R.T., M.G. Ryan, G.I. Ågren, H.E. Birge, E.A. Davidson, P.E. Eliasson, S.E. Evans, S.D. Frey, C.P. Giardina, F.M. Hopkins, R. Hyvonen, M.U.F. Kirschbaum, J.M. Lavallee, J. Liefeld, W.J. Parton, J.M. Steinweg, M.D. Wallenstain, J.A.M. Wetterstedt, and M.A. Bradford. 2011. Temperature and Soil Organic Matter Decomposition Rates Synthesis of Current Knowledge and a Way Forward. Global Change Biology 17, no.

11 (November 2011): 3392–3404.

Conen, F., J. Leifeld, B. Seth, and C. Alewell. 2006. Warming Mineralises Young and Old Soil Carbon Equally. Biogeosciences 3, no. 4 (2006): 515–19.

Cook, F.J., and V.A. Orchard. 2008. Relationships between Soil Respiration and Soil Moisture. Soil Biology & Biochemistry 40, no. 5 (May 2008): 1013–18.

Creamer, Courtney A., Lynne M. Macdonald, Jeff A. Baldock, Jonathan Sanderman, and Mark Farrell. “Divergent Responses of Organic Matter Composition to Incubation Temperature.” Geoderma 259–260 (December 2015): 279–87. doi:10.1016/j.geoderma.2015.06.019.

CSCAP (Cropping Systems Coordinated Agricultural Project). 2016. About. Sustainable Corn.Org - Crops, Climate, Culture and Change. http://www.sustainablecorn.org/About-Project/Index.html.

113

CSCAP. 2017. About This Project. Sustainable Corn.Org. Accessed March 1, 2017. https://sustainablecorn.org/About-Project/Index.html.

Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald, and J.-G. Winther. 2013. Introduction. In Climate Change 2013: The Physical Science Basis.

Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Cui, Junfang, and Nicholas M. Holden. The Relationship between Soil Microbial Activity and Microbial Biomass, Soil Structure and Grassland Management. Soil and Tillage Research, Soil Structure and its Functions in Ecosystems: Phase matter & Scale matter, 146, Part A (March 2015): 32–38. doi:10.1016/j.still.2014.07.005.

Davidson, E. A., I. A. Janssens, and Y. Q. Luo. 2006. On the Variability of Respiration in Terrestrial Ecosystems: Moving beyond Q(10). Global Change Biology 12, no. 2 (February 2006): 154–64. doi:10.1111/j.1365-2486.2005.01065.x.

Deurer, Markus, Siva Sivakumaran, Stefanie Ralle, Iris Vogeler, Ian Mclvor, Brent Clothier, Steve Green, and Joerg Bachmann. A New Method to Quantify the Impact of Soil Carbon Management on Biophysical Soil Properties: The Example of Two Apple Orchard Systems in New Zealand. Journal of Environmental Quality 37, no. 3 (June 2008): 915–24. doi:10.2134/jeq2007.0508.

Dick, W.A. 1983. Organic carbon, nitrogen, and phosphorus concentrations and pH in soil profiles as affected by tillage intensity. Soil Science Society of America Journal 47:102107.

114

Dick, W.A., D.M. Van Doren, Jr., G.B. Triplett, Jr., and J.E. Henry. 1986a. Influence of long-term tillage and rotation combinations on crop yields and selected soil parameters. II. Results obtained for a Typic Fragiudalf soil. Research Bulletin 1181.

Wooster, OH: Ohio Agricultural Research and Development Center, The Ohio State University.

Dick, W.A., D.M. Van Doren, Jr., G.B. Triplett, Jr., and J.E. Henry. 1986b. Influence of long-term tillage and rotation combinations on crop yields and selected soil parameters. I. Results obtained for a Mollic Ochraqualf soil. Research Bulletin 1180.

Wooster, OH: Ohio Agricultural Research and Development Center, The Ohio State University.

Dick, W.A., E.L. McCoy, W.M. Edwards, and R. Lal. 1991. Continuous application of notillage to Ohio soils. Agronomy Journal 83:65-73.

Dick, W.A., Triplett, G.B., and D.M. Van Doren. 2013. Triplett-Van Doren Long-term Tillage and Crop Rotation Data. Wooster, OH: Ohio Agricultural Research and Development Center, The Ohio State University. http://hdl.handle.net/1811/55716 Dijkstra, F.A., and J.A. Morgan. 2012. Elevated CO2 and Warming Effects on Soil Carbon Sequestration and Greenhouse Gas Exchange in Agroecosystems. In Managing Agricultural Greenhouse Gases, 467–86. Elsevier, 2012. http://linkinghub.elsevier.com/retrieve/pii/B9780123868978000279.

Dold, C., H. Büyükcangaz, W. Rondinelli, J.H. Prueger, T.J. Sauer, and J.L. Hatfield. 2017.

Long-Term Carbon Uptake of Agro-Ecosystems in the Midwest. Agricultural and Forest Meteorology 232 (January 2017): 128–40.

115

Dungait, Jennifer A. J., David W. Hopkins, Andrew S. Gregory, and Andrew P. Whitmore.

2012. Soil Organic Matter Turnover Is Governed by Accessibility Not Recalcitrance.

Global Change Biology 18, no. 6 (June 1, 2012): 1781–96. doi:10.1111/j.13652486.2012.02665.x.

ECCC (Environment and Climate Change Canada). 2016. The Third Generation Coupled Global Climate Model. ECCC. http://www.ec.gc.ca/ccmaccccma/default.asp?lang=En&n=1299529F-1 Erhagen, Björn, Mats Öquist, Tobias Sparrman, Mahsa Haei, Ulrik Ilstedt, Mattias Hedenström, Jürgen Schleucher, and Mats B. Nilsson. Temperature Response of Litter and Soil Organic Matter Decomposition Is Determined by Chemical Composition of Organic Material. Global Change Biology 19, no. 12 (December 2013): 3858–71. doi:10.1111/gcb.12342.

Edwards, A. P. and Bremner, J. M. 1967. Microaggregates in soils. J. Soil Sci. 18: 65–73.

Evrendilek, F., and M.K. Wali. 2001. Modelling Long-Term C Dynamics in Croplands in the Context of Climate Change: A Case Study from Ohio. Environmental Modelling & Software 16, no. 4 (June 2001): 361–75.

Evrendilek, F., and M.K. Wali. 2004. Changing Global Climate: Historical Carbon and Nitrogen Budgets and Projected Responses of Ohio’s Cropland Ecosystems.

Ecosystems 7, no. 4 (June 2004).

FAO and ITPS. 2015. Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy.

116

Feddema, Johannes. 1998. Estimated Impacts of Soil Degradation on the African Water Balance and Climate. Climate Reserach 10 (1998): 127–41.

Feddema, J. J., and S. Freire. Soil Degradation, Global Warming and Climate Impacts.

Climate Research 17, no. 2 (August 15, 2001): 209–16. doi:10.3354/cr017209.

Feng, Youzhi, Paul Grogan, J. Gregory Caporaso, Huayong Zhang, Xiangui Lin, Rob Knight, and Haiyan Chu. pH Is a Good Predictor of the Distribution of Anoxygenic Purple Phototrophic Bacteria in Arctic Soils. Soil Biology and Biochemistry 74 (July 2014): 193–200. doi:10.1016/j.soilbio.2014.03.014.

Franzluebbers, A. J. 1999. Microbial Activity in Response to Water-Filled Pore Space of Variably Eroded Southern Piedmont Soils. Applied Soil Ecology 11, no. 1 (January 3, 1999): 91–101. doi:10.1016/S0929-1393(98)00128-0.

Franzluebbers, A. J. 2002. Water Infiltration and Soil Structure Related to Organic Matter and Its Stratification with Depth. Soil & Tillage Research 66, no. 2 (July 2002): 197– 205. doi:10.1016/S0167-1987(02)00027-2.

Frey, Serita D., Juhwan Lee, Jerry M. Melillo, and Johan Six. The Temperature Response of Soil Microbial Efficiency and Its Feedback to Climate. Nature Climate Change 3, no. 4 (April 2013): 395–98. doi:10.1038/nclimate1796.

Georgiou, Katerina, Charles D. Koven, William J. Riley, and Margaret S. Torn. Toward Improved Model Structures for Analyzing Priming: Potential Pitfalls of Using Bulk Turnover Time. Global Change Biology 21, no. 12 (December 2015): 4298–4302. doi:10.1111/gcb.13039.

Goebel, Marc-O., Jörg Bachmann, Markus Reichstein, Ivan A. Janssens, and Georg Guggenberger. 2011. Soil Water Repellency and Its Implications for Organic Matter 117

Decomposition - Is There a Link to Extreme Climatic Events? Global Change Biology 17, no. 8 (August 2011): 2640–56. doi:10.1111/j.1365-2486.2011.02414.x.

Gomiero, Tiziano. Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge. Sustainability 8, no. 3 (March 2016). doi:10.3390/su8030281.

Gosling, Simon N., and Nigel W. Arnell. A Global Assessment of the Impact of Climate Change on Water Scarcity. Climatic Change 134, no. 3 (February 2016): 371–85. doi:10.1007/s10584-013-0853-x.

Greenland, D. J. 1965. Interactions between clays and organic compounds in soils. Part II. Adsorption of soil organic compounds and its effect on soil properties. Soils and Fert.

28: 521–527.

Gregorich, E.G., P. Rochette, A.J. VandenBygaart, and D.A. Angers. 2007. Greenhouse Gas Contributions of Agricultural Soils and Potential Mitigation Practices in Eastern Canada (vol 83, Pg 53, 2005). Soil & Tillage Research 94, no. 1 (May 2007): 262–63.

Hammons, S.K. 2009. Re-Examining the Relationship between Tillage Regime and Global Climate Change. Journal of Natural Resources and Life Sciences Education 38 (2009): 144–47.

Hatfield, J. 2012. Agriculture in the Midwest. In U.S. National Climate Assessment Midwest Technical Input Report. J. Winkler, J. Andresen, J. Hatfield, D. Bidwell, and D. Brown, coordinators. Available from the Great Lakes Integrated Sciences and Assessments (GLISA) Center, http://glisa.msu.edu/docs/NCA/MTIT_Agriculture.pdf Hatfield, J., G. Takle, R. Grotjahn, P. Holden, R.C. Izaurralde, T. Mader, E. Marshall, and D. Liverman. 2014. Ch. 6: Agriculture. In Climate Change Impacts in the United States:

118

The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 150-174.

Hill, Paul W., Mark H. Garnett, John Farrar, Zafar Iqbal, Muhammad Khalid, Nawaf Soleman, and Davey L. Jones. Living Roots Magnify the Response of Soil Organic Carbon Decomposition to Temperature in Temperate Grassland. Global Change Biology 21, no. 3 (March 2015): 1368–75. doi:10.1111/gcb.12784.

Hobbs, P.R., K. Sayre, and R. Gupta. 2008. The Role of Conservation Agriculture in Sustainable Agriculture. Philosophical Transactions of the Royal Society of London B: Biological Sciences 363, no. 1491 (February 12, 2008): 543–55.

Hopkins, F.M., T.R. Filley, G. Gleixner, M. Lange, S.M. Top, and S.E. Trumbore. 2014.

Increased Belowground Carbon Inputs and Warming Promote Loss of Soil Organic Carbon through Complementary Microbial Responses. Soil Biology & Biochemistry 76 (September 2014): 57–69.

Hudson, Berman D. 1994. Soil Organic Matter and Available Water Capacity. Journal of Soil and Water Conservation 49, no. 2 (1994): 189+.

IPCC. 2013. Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. In Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, 119

D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.

IPCC. 2014: Climate Change 2014: Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Janetos, A., L. Hansen, D. Inouye, B.P. Kelly, L. Meyerson, B. Peterson, and R. Shaw. 2008.

Biodiversity. In The effects of climate change on agriculture, land resources, water resources, and biodiversity in the United States. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Washington, DC. Jenkinson, D.S., P.B.S Hart, J.H. Rayner, and L.C. Parry. 1987. Modelling the turnover of organic matter in long-term experiments at Rothamsted. INTECOL Bulletin 15, 1-8.

Jimenez, R., and J.K. Ladha. 1993. Automated Elemental Analysis - a Rapid and Reliable but Expensive Measurement of Total Carbon and Nitrogen in Plant and Soil Samples.

Communications in Soil Science and Plant Analysis 24, no. 15–16 (1993): 1897–1924.

Jiménez Cisneros, B.E., T. Oki, N.W. Arnell, G. Benito, J.G. Cogley, P. Döll, T. Jiang, and S.S. Mwakalila, 2014: Freshwater resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, 120

A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229-269.

Jones, C., C. McConnell, K. Coleman, P. Cox, P. Falloon, D. Jenkinson, and D. Powlson.

2005. Global Climate Change and Soil Carbon Stocks; Predictions from Two Contrasting Models for the Turnover of Organic Carbon in Soil. Global Change Biology 11, no. 1: 154–66.

Kätterer, T., O. Andrén, and P-E. Jansson. Pedotransfer Functions for Estimating Plant Available Water and Bulk Density in Swedish Agricultural Soils. Acta Agriculturae Scandinavica, Section B - Plant Soil Science 56, no. 4 (December 2006): 263–76. doi:10.1080/09064710500310170.

Kaurin, Anela, Rok Mihelic, Damijana Kastelec, Michael Schloter, Marjetka Suhadolc, and Helena Grcman. 2015. Consequences of Minimum Soil Tillage on Abiotic Soil Properties and Composition of Microbial Communities in a Shallow Cambisol Originated from Fluvioglacial Deposits. Biology and Fertility of Soils 51, no. 8 (November 2015): 923–33. doi:10.1007/s00374-015-1037-9.

Kay, B. D. 1998. Soil structure and organic carbon: a review. In Soil Processes and the Carbon Cycle. pp. 169–197, Lal, R., Kimble, J. M., Follet, R. F., and Stewart B. A., Eds., CRC Press, Boca Raton, FL. Kemper, William Doral, Elbry James Koch, United States, and Department of Agriculture.

Aggregate Stability of Soils from Western United States and Canada; Measurement Procedure, Correlations with Soil Constituents. Washington: Agricultural Research Service, U.S. Dept. of Agriculture; [for sale by the Supt. of Docs., U.S. Govt. Print.

Off., 1966.

121

Kibblewhite, M.G., B.J. Chambers, and K.W.T. Goulding. 2016. How Good Is the Evidence to Support Investment in Soil Protection? Soil Use and Management 32 (June 1, 2016): 172–82.

Kieft, Thomas L., Edith Soroker, and Mary K. Firestone. 1987. Microbial Biomass Response to a Rapid Increase in Water Potential When Dry Soil Is Wetted. Soil Biology and Biochemistry 19, no. 2 (January 1, 1987): 119–26. doi:10.1016/0038-0717(87)90070-8.

Keiluweit, Marco, Jeremy J. Bougoure, Peter S. Nico, Jennifer Pett-Ridge, Peter K. Weber, and Markus Kleber. Mineral Protection of Soil Carbon Counteracted by Root Exudates. Nature Climate Change 5, no. 6 (June 2015): 588–95. doi:10.1038/nclimate2580.

Kirschbaum, M.U.F. 1995. The Temperature Dependence of Soil Organic Matter Decomposition, and the Effect of Global Warming on Soil Organic C Storage. Soil Biology and Biochemistry 27, no. 6: 753–60.

Kirschbaum, Miko Uwe Franz. 2006. The Temperature Dependence of Organic-Matter Decomposition—still a Topic of Debate. Soil Biology and Biochemistry 38, no. 9 (September 2006): 2510–18. doi:10.1016/j.soilbio.2006.01.030.

Kladivko, E.J., M.J. Helmers, L.J. Abendroth, D. Herzmann, R. Lal, M. Castellano, D.S. Mueller, J.E. Sawyer, R.P. Anex, R.W. Arritt, B. Basso, J.V. Bonta, L. Bowling, R.M. Cruse, N.R. Fausey, J. Frankenberger, P. Gassman, A.J. Gassmann, C.L. Kling, A. Kravchenko, J.G. Lauer, F.E. Miguez, E.D. Nafziger, N. Nkongolo, M. O'Neal, L.B. Owens, P. Owens, P. Scharf, M.J. Shipitalo, J.S. Strock, and M.B. Villamil. 2014.

Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, Special 122

Issue for Climate and Agriculture. 69(6):532-542. http://dx.doi.org/10.2489/jswc.69.6.532 Kummu, Matti, Philip J. Ward, Hans de Moel, and Olli Varis. Is Physical Water Scarcity a New Phenomenon? Global Assessment of Water Shortage over the Last Two Millennia. Environmental Research Letters 5, no. 3 (September 2010): 34006. doi:10.1088/1748-9326/5/3/034006.

Kuzyakov, Y. 2006. Sources of CO2 Efflux from Soil and Review of Partitioning Methods.

Soil Biology and Biochemistry 38, no. 3 (March 2006): 425–48. doi:10.1016/j.soilbio.2005.08.020.

Lal, R. 2004. Soil Carbon Sequestration to Mitigate Climate Change. Geoderma 123, no. 1–2 (November 2004): 1–22.

Lal, R. 2004. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science 304, no. 5677 (June 11, 2004): 1623–27.

Lal, R. 2010. Managing Soils and Ecosystems for Mitigating Anthropogenic Carbon Emissions and Advancing Global Food Security. BioScience 60, no. 9 (October 2010): 708–21.

Lal, R. 2014. Societal value of soil carbon. J.Soil Water Conserv. 69:186A-192A. Lal, R. 2016. Beyond COP 21: Potential and Challenges of the “4 per Thousand” Initiative.

Journal of Soil and Water Conservation 71, no. 1 (January 1, 2016): 20A–25A. Lal, R., J. A. Delgado, P. M. Groffman, N. Millar, C. Dell, and A. Rotz. 2011. Management to Mitigate and Adapt to Climate Change. Journal of Soil and Water Conservation 66, no. 4 (August 2011): 276–85.

123

Lal, R., K. Lorenz, R.R.K. Huttle, B.U. Schneider, and J.Von Braun (Eds). 2013. Ecosystem Services and Carbon Sequestration in the Biosphere. Springer, Dordrecht, Netherland, 464 pp.

Lal, Rattan and Manoj Shukla. Principles of Soil Physics. New York, NY: Marcel Dekker, Inc., 2004.

Lawrence, Corey R., Jason C. Neff, and Joshua P. Schimel. 2009. Does Adding Microbial Mechanisms of Decomposition Improve Soil Organic Matter Models? A Comparison of Four Models Using Data from a Pulsed Rewetting Experiment. Soil Biology and Biochemistry 41, no. 9 (September 2009): 1923–34. doi:10.1016/j.soilbio.2009.06.016.

Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero , L. Bopp, J. Chang, F. Chevallier, L.P. Chini, P. Ciais, M. Fader, R. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A.K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, JEMS Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng. 2015. Global Carbon Budget 2015.

Earth System Science Data.

124

Leakey, A.D.B. 2006. Photosynthesis, Productivity, and Yield of Maize Are Not Affected by Open-Air Elevation of CO2 Concentration in the Absence of Drought. Plant Physiology 140, no. 2 (January 13, 2006): 779–90.

Lee, J., J.W. Hopmans, D.E. Rolston, S.G. Baer, and J. Six. 2009. Determining Soil Carbon Stock Changes: Simple Bulk Density Corrections Fail. Agriculture, Ecosystems & Environment 134, no. 3–4 (December 2009): 251–56.

Littlefield, B. Z., J. L. Ehman, W. Fan, J. J. Johnston, B. Offerle, and J. C. Randolph. 1998.

Climate Change Effects on Agricultural Productivity in the Midwestern Great Lakes Region. Edited by S. A. ElSwaify and D. S. Yakowitz. Boca Raton: Lewis Publishers Inc, 1998.

Loveland, P, and J Webb. Is There a Critical Level of Organic Matter in the Agricultural Soils of Temperate Regions: A Review. Soil and Tillage Research 70, no. 1 (March 2003): 1–18. doi:10.1016/S0167-1987(02)00139-3.

Lu, X., and Q. Zhuang. 2010. Evaluating Climate Impacts on Carbon Balance of the Terrestrial Ecosystems in the Midwest of the United States with a Process-Based Ecosystem Model. Mitigation and Adaptation Strategies for Global Change 15, no. 5 (June 2010): 467–87.

Makipaa, R., M. Hakkinen, P. Muukkonen, and M. Peltoniemi. 2008. The Costs of Monitoring Changes in Forest Soil Carbon Stocks. Boreal Environment Research 13 (2008): 120–30.

Malamoud, K., A.B. McBratney, B. Minasny, and D.J. Field. 2009. Modelling how carbon affects soil structure. Geoderma 149:19–26. doi:10.1016/j.geoderma.2008.10.018.

125

Mangalassery, S., S. Sjoegersten, D.L. Sparkes, and S.J. Mooney. 2015. Examining the Potential for Climate Change Mitigation from Zero Tillage. Journal of Agricultural Science 153, no. 7 (September 2015): 1151–73.

Manns, Hida R., and Aaron A. Berg. Importance of Soil Organic Carbon on Surface Soil Water Content Variability among Agricultural Fields. Journal of Hydrology, Determination of soil moisture: Measurements and theoretical approaches, 516 (August 4, 2014): 297–303. doi:10.1016/j.jhydrol.2013.11.018.

Manzoni, Stefano, Joshua P. Schimel, and Amilcare Porporato. 2012. Responses of Soil Microbial Communities to Water Stress: Results from a Meta-Analysis. Ecology 93, no.

4 (April 1, 2012): 930–38. doi:10.1890/11-0026.1.

MATLAB. 2016. Version 9.0.0.341460 (R2016a). The MathWorks, Inc., Natick, Massachusetts, United States.

Mays, Neal, Curt Richard Rom, Kristofor R. Brye, Mary C. Savin, and M. Elena Garcia.

Groundcover Management System and Nutrient Source Impacts on Soil Quality Indicators in an Organically Managed Apple (Malus X Domestica Borkh.) Orchard in the Ozark Highlands. Hortscience 50, no. 2 (February 2015): 295–303.

Mearns, L.O., S. McGinnis, S. Biner, D. Caya, and R. Laprise. 2007, updated 2014. The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2016-05-05.

Mearns, L. O., S. Sain, L.R. Leung, M.S. Bukovsky, S. McGinnis, S. Biner, D. Caya, R.W. Arritt, W. Gutowski, E. Takle, M. Snyder, R.G. Jones, A.M.B. Nunes, S. Tucker, D. Herzmann, L. McDaniel, L. Sloan. 2013. Climate Change Projections of the North 126

American Regional Climate Change Assessment Program (NARCCAP). Climatic Change 120, no. 4 (October 2013): 965–75.

Mekonnen, M. M., and A. Y. Hoekstra. Four Billion People Facing Severe Water Scarcity.

Science Advances 2, no. 2 (February 12, 2016): e1500323–e1500323. doi:10.1126/sciadv.1500323.

Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds. 2014: Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp. doi:10.7930/J0Z31WJ2.

Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston. 2012a. Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. Annual Summaries of the Global Historical Climatology Network - Daily. NOAA National Climatic Data Center. October 8, 2015.

Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston. 2012b. An Overview of the Global Historical Climatology Network-Daily Database. J. Atmos. Oceanic Technol., 29, 897-910.

Miller, Amy E., Joshua P. Schimel, Thomas Meixner, James O. Sickman, and John M. Melack. 2005. Episodic Rewetting Enhances Carbon and Nitrogen Release from Chaparral Soils. Soil Biology and Biochemistry 37, no. 12 (December 2005): 2195– 2204. doi:10.1016/j.soilbio.2005.03.021.

Min, Kyungjin, Chris Freeman, Hojeong Kang, and Sung-Uk Choi. The Regulation by Phenolic Compounds of Soil Organic Matter Dynamics under a Changing Environment. BioMed Research International 2015 (October 1, 2015): e825098. doi:10.1155/2015/825098.

127

Mishra, U., R. Lal, D. Liu, and M. Van Meirvenne. 2010. Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale. Soil Science Society of America Journal 74, no. 3 (2010): 906.

Moinet, Gabriel Y. K., Ellen Cieraad, John E. Hunt, Anitra Fraser, Matthew H. Turnbull, and David Whitehead. 2016. Soil Heterotrophic Respiration Is Insensitive to Changes in Soil Water Content but Related to Microbial Access to Organic Matter. Geoderma 274 (July 15, 2016): 68–78. doi:10.1016/j.geoderma.2016.03.027.

Moran, K.K., and J.D. Jastrow. 2010. Elevated Carbon Dioxide Does Not Offset Loss of Soil Carbon from a Corn–soybean Agroecosystem. Environmental Pollution, Facing the Future: Evidence from Joint AspenFACE, SoyFACE and SFB 607 Meeting, 158, no. 4 (April 2010): 1088–94.

Moyano, Fernando E., Stefano Manzoni, and Claire Chenu. 2013. Responses of Soil Heterotrophic Respiration to Moisture Availability: An Exploration of Processes and Models. Soil Biology and Biochemistry 59 (April 2013): 72–85. doi:10.1016/j.soilbio.2013.01.002.

Moyano, Fernando E., Stefano Manzoni, and Claire Chenu. Responses of Soil Heterotrophic Respiration to Moisture Availability: An Exploration of Processes and Models. Soil Biology and Biochemistry 59 (April 2013): 72–85. doi:10.1016/j.soilbio.2013.01.002.

Mungare, T.S., A.K. Shingte, and K.S. Pharande. Relationship of Available Water Capacity with Some of the Physical Properties of Soils. Journal of Maharashtra Agricultural Universities 8, no. 1 (1983): 9–13.

128

NARCCAP (North American Regional Climate Change Assessment Program). 2016a.

NCEP Seasonal Climate. NARCCAP. http://www.narccap.ucar.edu/results/ncepresults.html.

NARCCAP. 2016b. The A2 Emissions Scenario. NARCCAP. http://www.narccap.ucar.edu/about/emissions.html.

NARCCAP. 2016c. Atmosphere-Ocean General Circulation Models (AOGCMs).

NARCCAP. http://www.narccap.ucar.edu/about/aogcms.html NARCCAP. 2016d. Scatter Plots. http://narccap.ucar.edu/results/scatter/index.html.

NRCS (National Resources Conservation Service). 2016. Soil Texture Calculator | NRCS Soils. NRCS. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_054 167.

Olness, A., and D. Archer. 2005. Effect of Organic Carbon on Available Water in Soil. Soil Science 170, no. 2 (February 2005): 90–101. doi:10.1097/01.ss.0000155496.63323.35.

Osanai, Y., J. K. Janes, P.C.D. Newton, and M.J. Hovenden. 2015. Warming and Elevated CO2 Combine to Increase Microbial Mineralisation of Soil Organic Matter. Soil Biology & Biochemistry 85 (June 2015): 110–18.

Pan, Z., D. Andrade, M. Segal, J. Wimberley, N. McKinney, and E. Takle. 2010. Uncertainty in Future Soil Carbon Trends at a Central U.S. Site under an Ensemble of GCM Scenario Climates. Ecological Modelling 221, no. 5 (March 2010): 876–81.

Pang, Xueyong, Biao Zhu, Xiaotao Lü, and Weixin Cheng. Labile Substrate Availability Controls Temperature Sensitivity of Organic Carbon Decomposition at Different Soil

129

Depths. Biogeochemistry 126, no. 1–2 (November 2015): 85–98. doi:10.1007/s10533015-0141-0.

Panagopoulos, Y., P.W. Gassman, R.W. Arritt, D.E. Herzmann, T.D. Campbell, A. Valcu, M.K. Jha, C.L. Kling, R. Srinivasan, M. White, and J.G. Arnold. 2015. Impacts of Climate Change on Hydrology, Water Quality and Crop Productivity in the OhioTennessee River Basin. International Journal of Agricultural and Biological Engineering 8, no. 3 (2015): 1–18.

Paul, K. I., P. J. Polglase, A. M. O’Connell, J. C. Carlyle, P. J. Smethurst, and P. K. Khanna.

2003. Defining the Relation between Soil Water Content and Net Nitrogen Mineralization. European Journal of Soil Science 54, no. 1 (March 2003): 39–48. doi:10.1046/j.1365-2389.2003.00502.x.

Paustian, K., J. Lehmann, S. Ogle, D. Reay, G.P. Robertson, and P. Smith. 2016. ClimateSmart Soils. Nature 532, no. 7597: 49–57.

Pereira P., E. Isadora, H. Chung, K. Scow, and J. Six. 2013. Microbial Communities and Soil Structure Are Affected by Reduced Precipitation, but Not by Elevated Carbon Dioxide.

Soil Science Society of America Journal 77, no. 2 (2013): 482.

Pietri, J. C. A., and P. C. Brookes. Relationships between Soil pH and Microbial Properties in a UK Arable Soil. Soil Biology and Biochemistry 40, no. 7 (July 2008): 1856–61. doi:10.1016/j.soilbio.2008.03.020.

Pribyl, Douglas W. A Critical Review of the Conventional SOC to SOM Conversion Factor.

Geoderma 156, no. 3–4 (May 15, 2010): 75–83. doi:10.1016/j.geoderma.2010.02.003.

Pryor, S.C., D. Scavia, C. Downer, M. Gaden, L. Iverson, R. Nordstrom, J. Patz, and G.P. Robertson. 2014. Ch. 18: Midwest. Climate In Change Impacts in the United States: 130

The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 418-440.

Rajan, K., A. Natarajan, K. S. Anil Kumar, M. S. Badrinath, and R. C. Gowda. Soil Organic Carbon - the Most Reliable Indicator for Monitoring Land Degradation by Soil Erosion. Current Science (00113891) 99, no. 6 (September 25, 2010): 823–27.

Rawls, W. J., Y. A. Pachepsky, J. C. Ritchie, T. M. Sobecki, and H. Bloodworth. 2003. Effect of Soil Organic Carbon on Soil Water Retention. Geoderma, Quantifying agricultural management effects on soil properties and processes, 116, no. 1–2 (September 2003): 61–76. doi:10.1016/S0016-7061(03)00094-6.

Reeves, D. W. 1997. The Role of Soil Organic Matter in Maintaining Soil Quality in Continuous Cropping Systems. Soil & Tillage Research 43, no. 1–2 (November 1, 1997): 131–67.

Reinsch, Sabine, Per Ambus, Barry Thornton, and Eric Paterson. Impact of Future Climatic Conditions on the Potential for Soil Organic Matter Priming. Soil Biology & Biochemistry 65 (October 2013): 133–40. doi:10.1016/j.soilbio.2013.05.013.

Rousk, Johannes, Erland Bååth, Philip C. Brookes, Christian L. Lauber, Catherine Lozupone, J. Gregory Caporaso, Rob Knight, and Noah Fierer. Soil Bacterial and Fungal Communities across a pH Gradient in an Arable Soil. The ISME Journal 4, no. 10 (October 2010): 1340–51. doi:10.1038/ismej.2010.58.

Saxton, K. E., and W. J. Rawls. 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Science Society of America Journal 70, no. 5 (October 2006): 1569–78.

131

Schimel, Joshua, Teri C. Balser, and Matthew Wallenstein. 2007. Microbial Stress-Response Physiology and Its Implications for Ecosystem Function. Ecology 88, no. 6 (2007): 1386–94.

Schjønning, Per, Ingrid K. Thomsen, Per Moldrup, and Bent T. Christensen. Linking Soil Microbial Activity to Water- and Air-Phase Contents and Diffusivities. Soil Science Society of America Journal 67, no. 1 (2003): 156. doi:10.2136/sssaj2003.1560.

Southworth, J, R. Pfeifer, M. Habeck, J. Randolph, O. Doering, and D. Rao. 2002. Sensitivity of Winter Wheat Yields in the Midwestern United States to Future Changes in Climate, Climate Variability, and CO2 Fertilization. Climate Research 22 (2002): 73–86.

Spaccini, R., A. Piccolo, J.S.C. Mbagwu, A.Z. Teshale, and C.A. Igwe. 2002. Influence of the addition of organic residues on carbohydrate content and structural stability of some highland soils in Ethiopia. Soil Use Manage. 18:404–411. doi:10.1079/SUM2002152.

Stocking, A., F. H. Obando, and A. Tengberg. Soil Degradation and Its Impact on Plants in Drylands. In Sustainable Use and Management of Soils - Arid and Semiarid Regions, edited by A. F. Cano, R. O. Silla, and A. R. Mermut, 36:355–68, 2005.

Sulman, Benjamin N., Richard P. Phillips, A. Christopher Oishi, Elena Shevliakova, and Stephen W. Pacala. Microbe-Driven Turnover Offsets Mineral-Mediated Storage of Soil Carbon under Elevated CO2. Nature Climate Change 4, no. 12 (December 2014): 1099–1102. doi:10.1038/NCLIMATE2436.

Thornthwaite, C.W. and J.R. Mather. 1955. The Water Balance. Publications in Climatology, Vol 8, No. 1, Centerton, N.J., Laboratory of Climatology.

Tisdall, J. M. and Oades, J. M. 1982. Organic matter and water stable aggregates in soils. J. Soil Sci. 33: 141–163.

132

Uri, N.D. 2001. The Potential Impact of Conservation Practices in US Agriculture on Global Climate Change. Journal of Sustainable Agriculture 18, no. 1 (2001): 109–31.

USDA-NRCS. 2005. MLRA Definitions | NRCS Soils. December 15, 2005. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2 _053625 USDA-NRCS. 2006. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: L - Lake State Fruit, Truck Crop, and Dairy Region; 99 - Erie-Huron Lake Plain. Major Land Resource Regions Custom Report, 2006. http://apps.cei.psu.edu/mlra/.

USDA-NRCS. 2006. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: R - Northeastern Forage and Forest Region 139 - Lake Erie Glaciated Plateau. Major Land Resource Regions Custom Report, 2006. http://apps.cei.psu.edu/mlra/.

USDA-NRCS. 2006. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin: M – Central Feed Grains and Livestock Region 111B – Indiana and Ohio Till Plain, Northeastern Part. Major Land Resource Regions Custom Report, 2006. http://soils.usda.gov/MRLAExplorer. van Groenigen, K.J., J. Six, B.A. Hungate, M. de Graaff, N. van Breemen, and C. van Kessel.

2006. Element Interactions Limit Soil Carbon Storage. Proceedings of the National Academy of Sciences 103, no. 17 (April 25, 2006): 6571–74. van Groenigen, K.J., X. Qi, C. Osenberg, Y. Luo, and B. Hungate. 2014. Faster Decomposition Under Increased Atmospheric CO2 Limits Soil Carbon Storage. Science 344 (May 2, 2014): 508–9.

133

VandenBygaart, A.J. 2016. The Myth That No-till Can Mitigate Global Climate Change.

Agriculture Ecosystems & Environment 216 (January 15, 2016): 98–99.

Vörösmarty, Charles J., Pamela Green, Joseph Salisbury, and Richard B. Lammers. 2000.

Global Water Resources: Vulnerability from Climate Change and Population Growth.

Science 289, no. 5477 (July 14, 2000): 284–88. doi:10.1126/science.289.5477.284.

Wall, A., and J. Heiskanen. Water-Retention Characteristics and Related Physical Properties of Soil on Afforested Agricultural Land in Finland. Forest Ecology and Management 186, no. 1–3 (December 5, 2003): 21–32. doi:10.1016/S0378-1127(03)00239-1.

Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, and R. Somerville. 2014: Ch. 2: Our Changing Climate. In Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 19-67. doi:10.7930/J0KW5CXT. Wang, T., D.A. Wedin, and V.A. Zlotnik. 2009. Field evidence of a negative correlation between saturated hydraulic conductivity and soil carbon in a sandy soil. Water Resour.

Res. 45:W00A11. doi:10.1029/2007WR006768.

Wieder, William R., Gordon B. Bonan, and Steven D. Allison. Global Soil Carbon Projections Are Improved by Modelling Microbial Processes. Nature Climate Change 3, no. 10 (October 2013): 909–12. doi:10.1038/nclimate1951.

Wild, A. 1988. Russell’s soil conditions and plant growth. 11th ed., JohnWiley & Sons, Inc. New York.

134

Williams, J., R.E. Prebble, W.T. Williams, and C.T. Hignett. 1983. The Influence of Texture, Structure and Clay Mineralogy on the Soil Moisture Characteristic. Australian Journal of Soil Research 21, no. 1 (1983): 15.

Wutzler, T., and M. Reichstein. Priming and Substrate Quality Interactions in Soil Organic Matter Models. Biogeosciences 10, no. 3 (2013): 2089–2103. doi:10.5194/bg-10-20892013.

Xie, H., K. Liu, D. Sun, Z. Wang, X. Lu, and K. He. 2015. A Field Experiment with Elevated Atmospheric CO2-Mediated Changes to C4 Crop-Herbivore Interactions.

Scientific Reports 5 (September 18, 2015): 13923.

Zhu, Biao, Jessica L. M. Gutknecht, Donald J. Herman, Daniel C. Keck, Mary K. Firestone, and Weixin Cheng. Rhizosphere Priming Effects on Soil Carbon and Nitrogen Mineralization. Soil Biology & Biochemistry 76 (September 2014): 183–92. doi:10.1016/j.soilbio.2014.04.033.

Zhuang, J., J.F. McCarthy, E. Perfect, L.M. Mayer, and J.D. Jastrow. 2008. Soil water hysteresis in water-stable microaggregates as affected by organic matter. Soil Sci. Soc. Am. J. 72:212–220. doi:10.2136/sssaj2007.0001S6.

Zong, Y., and Z. Shangguan. 2014. Nitrogen Deficiency Limited the Improvement of Photosynthesis in Maize by Elevated CO2 Under Drought. Journal of Integrative Agriculture 13, no. 1 (January 2014): 73–81.

135