Nc

Sa

b

c

a

ARR1AA

KNCSCSMC

1

rePaBpdpSdl

b

eT

0d

Agriculture, Ecosystems and Environment 149 (2012) 37– 49

Contents lists available at SciVerse ScienceDirect

Agriculture, Ecosystems and Environment

jo ur n al homepage: www.elsev ier .com/ lo cate /agee

o-till management impacts on crop productivity, carbon input and soilarbon sequestration

tephen M. Oglea,b,∗, Amy Swana, Keith Paustiana,c

Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USADepartment of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USADepartment of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA

r t i c l e i n f o

rticle history:eceived 28 March 2011eceived in revised form6 December 2011ccepted 18 December 2011vailable online 11 January 2012

eywords:o-tillrop yieldsoil organic carbon

a b s t r a c t

The efficacy of no-till agriculture for increasing C in soils has been questioned in recent studies. This is aserious issue after many publications and reports during the last two decades have recommended no-tillas a practice to mitigate greenhouse gas emissions through soil C sequestration. Our objective was toinvestigate the possibility that the lack of C increase in some no-till systems may be due to changes incrop productivity and subsequent C input to soils. A meta-analysis of 74 published studies was conductedto determine if crop production varies between no-till and full tillage management. The results were usedto estimate the change in C input due to no-till adoption and the influence on soil organic C stocks atsteady-state using the Century model. We found that crop productivity can be reduced with adoptionof no-till, particularly in cooler and/or wetter climatic conditions. The influence varies, however, andcrop productivity can even increase in some regions following adoption of no-till. In cases where crop

enturyoil organic C modelingeta-analysis

arbon sequestration

production and C inputs decreased due to no-till, the potential reduction in soil organic C stocks wasoffset by a decrease in soil C decomposition rates, except in cases where C inputs declined by 15% or more.Challenges still remain for understanding the full impact of no-till adoption on soil organic C stocks, suchas changes on C inputs in deeper subsurface horizons, the influence of variation in NT seeding methodson soil disturbance, and changes in SOM stabilization due to saturation limits in mineral soil fractions,which may further modify net C storage in soils.

. Introduction

Adoption of no-till (NT) management in croplands has beenecommended as a way to sequester C in soils (Follett, 2001; Lalt al., 1998; Ogle et al., 2005; Paustian et al., 1997b; West andost, 2002), but some recent studies have questioned whether NTctually increases soil organic C (SOC) stocks (Baker et al., 2007a;lanco-Canqui and Lal, 2008; Christopher et al., 2009). For exam-le, Baker et al. (2007a) suggested that tilled soils may support aeeper rooting pattern in crops, leading to higher C input below thelow layer (i.e., generally 20–25 cm), negating increases in surfaceOC under NT. Moreover, relatively few studies have evaluated Cynamics associated with tillage management deeper in the profile

eading to uncertainty in the impact of NT adoption on soil C stocks.It has been postulated that NT increases C in the plow layer

ecause of changes in soil structure and reduction in decomposition

∗ Corresponding author at: Natural Resource Ecology Laboratory, Campus Deliv-ry 1499, Colorado State University, Fort Collins, CO 80523, USA.el.: +1 970 491 7662; fax: +1 970 491 1965.

E-mail address: ogle@nrel.colostate.edu (S.M. Ogle).

167-8809/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.agee.2011.12.010

© 2011 Elsevier B.V. All rights reserved.

rates with physical protection of C within aggregates (Jastrow et al.,1996; Six et al., 2000, 1998). Six et al. (2000) proposed a modelin which microaggregates form within larger macroaggregates,and the microaggregates become more stable over time in a NTsystem. Moreover, the C in the microaggregates is less suscep-tible to decomposition when released from the macroaggregatestructure. Frequent tillage disrupts microaggregate formation,and consequently the C is not as well protected from microbialdecomposition.

Adoption of NT also has other impacts on carbon dynamics,particularly greater stratification of C in a NT system with higherC concentrations near the surface, while plowing tends to mixthe C throughout the top layer of the soil (Angers et al., 1997;Doran et al., 1998; Franzluebbers, 2002; Yang and Wander, 1999).Meta-analyses have shown that C stocks significantly increase inthe top 30 cm of soil profiles on average even with stratification(Ogle et al., 2005; West and Post, 2002), which is consistent withthe aggregate turnover model (Six et al., 2000). However, thereare case studies where C did not increase in the top 30 cm. For

example, Ogle et al. (2005) found that SOC did not increase insurface soil layers of about 10% of the experimental treatmentsthat were included in their meta-analysis. These cases are not

3 tems a

cefiss

tGWd21ebimFKylasGwBRaob1ebwlK12

imcpTOoslfrCt

2

2

iswnbrfm

8 S.M. Ogle et al. / Agriculture, Ecosys

onsistent with the aggregate turnover model suggested by Sixt al. (2000), but may possibly be explained by lower C inputrom crop production following NT adoption. Moreover, lower Cnput may also explain the apparent lack of C sequestration in NTystems when comparing C stocks from NT and full tillage (FT)ystems at deeper depths in the profile (Baker et al., 2007a).

Numerous studies have shown decreased crop yields with adop-ion of NT (Al-Kaisi et al., 2005; Cox et al., 1992; Drury et al., 2003;riffith et al., 1988; Hammel, 1995; Potter et al., 1996; Wilhelm andortmann, 2004), although there are also many studies that have

emonstrated no-change or even an increase in yield (Beyaert et al.,002; Dam et al., 2005; Dick and Van Doren, 1985; Edwards et al.,988; Halvorson et al., 1999, 2002; Hussain et al., 1999; Nyakatawat al., 2000; Tarkalson et al., 2006). Declines in yields have ofteneen attributed to cool and wet climatic conditions; crop growth

s reduced in the spring due to a surface residue layer under NTanagement that depresses temperatures in the soil relative to

T systems with little or no surface residues (Dwyer et al., 1995;itur et al., 1994). Poorly drained soils may experience reducedields with the adoption of NT due to the surface residues andower soil temperatures (Iragavarapu and Randall, 1995), as wells irrigated systems, which can have lower soil temperatures withurface residue early in the growing season (Sims et al., 1998).upta (1985) found that the planting date was delayed by 1–2eeks, particularly in the northern portion of the Midwestern Cornelt, due to the effect of surface residues on soil temperatures.educed N availability under NT management has been suggesteds a cause of lower yields relative to FT (Matowo et al., 1997);ther studies have suggested that NT can lead to reduced yieldsecause of greater susceptibility to disease (Dick and Van Doren,985; Dill-Macky and Jones, 2000) and weed infestation (Koenningt al., 1995). Conversely, yields can increase with NT managementecause improved soil structure and surface residue cover enhanceater infiltration, root growth and/or reduce evaporative water

osses from the soil (Diaz-Zorita et al., 2004; Griffith et al., 1988;arlen et al., 1996; Kennedy and Hutchinson, 2001; Lindwall et al.,995; McAndrew et al., 1994; Norwood, 1994; Tarkalson et al.,006).

If yields are changing with adoption of NT management, then Cnputs to the soil are likely to be modified relative to the previous FT

anagement system, and this would influence soil C stocks whichhange over time in response to differences in C inputs from croproduction and decomposition in the soil (Paustian et al., 1997c).hus, changing C inputs could alter the amount of C stored in soils.ur objective was to evaluate the influence of NT managementn yields, C inputs and SOC stocks, relative to tilled systems foreveral major commodity crops in the U.S. Evaluating the under-ying mechanisms driving C dynamics with NT adoption is criticalor understanding the conditions under which NT may increase oreduce C storage in soils. This study focuses on the possibility that

inputs could be changing with adoption of NT and contributing tohe lack of C sequestration that has been found in several studies.

. Materials and methods

.1. Literature review

A literature review was conducted to compile results from stud-es evaluating change in yield with NT adoption. Studies wereelected based on the following criteria: (a) well documented studyith a replicated experimental design and control treatments, (b)o change in crop types or management practices other than tillage

etween the treatments, including no differences in fertilizationates or a full factorial design with the same crops and levels ofertilizer applied to each tillage treatment; and (c) yield data for a

ajor commodity crop grown in the U.S.

nd Environment 149 (2012) 37– 49

Experiments were also included from Canada if there was sim-ilarity to the cropping management practices, soil and climaticconditions in northern croplands of the U.S.

Seventy-four published studies met the criteria for this anal-ysis (Table 1), with 1040 tillage treatment comparisons betweenFT and NT. These studies were located in the U.S. and southernCanada. However, there were no studies from the southwesternU.S. or California so these regions were not considered in our anal-ysis. Treatments included FT, reduced tillage and NT, but we focusedon the comparisons of FT and NT treatments, which have a largerdifference in SOC stocks (Ogle et al., 2005), and have been the focusof the debate associated with the impact of NT on carbon stocks(Baker et al., 2007a,b). FT is defined as a practice that substantiallymixes the soil using one of the following implements: moldboardplow, disk plow, disk chisel, twisted point chisel plow, heavy dutyoffset disk, subsoil chisel plow, bedder or disk ripper. Treatmentswere also classified as FT if more than one pass was made acrossthe experimental plot with a chisel plow, single disk, tandem disk,offset disk-light duty, one-way disk, heavy duty cultivator, ridgetill, or rototiller. NT is a management system with crops seededin previously unprepared soil using only planters or drills withattached coulters, disks or openers that create a narrow slot for theseed (Dickey et al., 1992, Young, 1982). Furthermore, fertilizer andmanure applications may only disturb the seed slot with no associ-ated full-width tillage (USDA-NRCS, 2010). There are also differenttypes of seeders and associated slot shapes that create variation insoil disturbance within the broad classification of NT (Baker et al.,2007b). However, many of the studies did not provide enough detailto classify this level of variation.

Corn was the most common crop in the experiments, but severalother major commodity crops were analyzed, including soybeans,winter wheat, spring wheat, cotton, and sorghum (Table 1). A fewother crops were also found in the literature review, such as barleyand canola, but were not analyzed due to the limited number ofstudies.

2.2. Crop yield analysis

Data were analyzed with a linear mixed-effect modeling tech-nique, which is a regression method that includes both fixed andrandom effects (Pinheiro and Bates, 2000). A separate model wasderived for each crop type. The general structure of the model is asfollows:

�Y = ˇ1X1 + · · · + ˇpXp + �site + �year∗site + ε, (1)

where �Y is the yield response variable representing the differencein yield between the NT and FT treatments; X’s are known covari-ates, i.e., fixed effects; ˇ’s are unknown regression coefficients tobe estimated from the data; �site and �year*site are random effectsaddressing correlation structure in the data from an individual siteand a time series within a site; and ε is the residual error.

Several fixed effects were tested in model development includ-ing mean annual temperature (◦C), mean annual precipitation(mm), N fertilization rate (kg ha−1), soil texture (proportion ofsand), and organic amendment rate (kg ha−1). Indicator variableswere used to identify studies with irrigation, presence of a covercrop, use of pesticides and hydric soil condition. All possible first-order interactions were tested in the statistical analysis. Fixedeffects and first-order interactions were considered significant atan alpha level of 0.05, in addition to reducing the Akaike Informa-tion Criteria (Akiake, 1973; Burnham and Anderson, 2002) by atleast a value of 2. In addition, the intercept was only included in a

model if the coefficient was significant at an alpha level of 0.05. Notethat fixed effects were included in the model that did not meet thespecific level of significance if an associated first-order interactionwith another variable did meet the alpha level of 0.05.

S.M. Ogle et al. / Agriculture, Ecosystems and Environment 149 (2012) 37– 49 39

Table 1Crop tillage studies included in the analysis.

Authors Location Number of years in study Irrigation Crop(s) evaluated

Aflakpui et al. (1994) Woodstock, ON 1–2 N CornAl-Kaisi et al. (2005) Ames, IA 2 N Corn, soybeanAnders et al. (2005) Stuttgart, AR 2–3 Y, N SoybeanBeyaert et al. (2002) Delhi, ON 1–3 N CornCassel and Wagger (1996) Salisbury, NC 1–2 Y, N CornCox et al. (1992) Central New York 1–3 N CornDam et al. (2005) Ste-Anne-de-Bellevue, QE 1–12 N CornDiaz-Zorita et al. (2004) Princeton, KY 2–8 N Corn, soybean, winter wheatDick and Van Doren (1985) Wooster, OH 4–20 N CornDick and Van Doren (1985) Hoytville, OH 4–20 N CornDick and Van Doren (1985) Crosby, OH 4–20 N CornDill-Macky and Jones (2000) Morris, MN 2 Y, N Corn, soybean, spring wheatDrinkwater et al. (2000) Rodale, PA 3 N CornDrury et al. (2003) Woodslee, ON 1–4 N CornDwyer et al. (1995) Ottawa, ON 1–3 N CornEdwards et al. (1988) Crossville, GA 1–4 N Corn, soybeanEghball and Power (1999) Mead, NE 1–4 N CornEpplin et al. (1994) Lahoma, OK 1–10 N Winter wheatGuy and Cox (2002) Genesee, ID 1–3 N Winter wheatHairston et al. (1990) Brooksville, MS 1–3 N SoybeanHairston et al. (1990) Raymond, MS 1–3 N SoybeanHairston et al. (1990) Newton, MS 2–3 N SoybeanHalvorson et al. (1999) Mandan, ND 1–12 N Spring wheat, winter wheatHammel (1995) Moscow, ID 1–4 N Winter wheatHargrove (1985) Williamson, GA 1 Y CornHunt et al. (2004) Florence, SC 2–5 N Corn, soybean, winter wheatHussain et al. (1999) Dixon Springs, IL 1–8 N Corn, soybeanIsmail et al. (1994) Lexington, KY 1–21 N CornJensen et al. (2004) Verndale, MN 1 N SoybeanJohnson et al. (2001) Tifton, GA 4 N CottonKarlen et al. (1991) Nashua, IA 1–12 N Corn, soybeanKarlen et al. (1996) Florence, SC 9–13 N Corn, cotton, winter wheatKarunatilake et al. (2000) Willsboro, NY 1–3 N CornKennedy and Hutchinson (2001) St. Joseph, LA 1–4 Y CottonKessavalou and Walters (1997) Mead, NE 1–3 Y CornKettler et al. (2000) Sidney, NE 1–5 N Winter wheatKitur et al. (1994) Dixon Springs, IL 1–3 N CornKoenning et al. (1995) Plymouth, NC 2–7 N SoybeanKurle et al. (2001) Janesville/Waunakee, WI 2 N SoybeanLal (1996) Hoytville, OH 1–7 N Corn, soybeanLarney and Lindwall (1994) Lethbridge, AB 1–8 N Winter wheatLinden et al. (2000) Rosemount, MN 2–13 N CornLindwall et al. (1995) Lethbridge. AB 1–9 N Winter wheatLiu et al. (2004) Elora, ON 1 N CornLiu et al. (2004) Woodstock, ON 1 N CornLowery et al. (1998) Arena, WI 1–5 Y, N Corn, soybeanLyon et al. (1998) Sidney, NE 26 N Winter wheatMatowo et al. (1997) Manhattan, KS 1–11 N SorghumMcConkey et al. (1996) Cantuar, SK 1–11 N Spring wheatMcConkey et al. (1996) Stewart Valley, SK 2–11 N Spring wheatMcConkey et al. (1996) Swift Current, SK 1–12 N Spring wheatMoschler et al. (1972) Blacksburg, VA 9 N CornMoschler et al. (1972) Orange, VA 6 N CornMoschler et al. (1972) Charlotte Courthouse, VA 5 N CornMueller et al. (2002) Dekalb, IL 1–3 N SoybeanNyakatawa et al. (2000) Belle Mina, AL 1–2 N CottonOpoku et al. (1997) Centralia, ON 1–2 N CornOpoku et al. (1997) Wyoming, ON 1–2 N CornPotter et al. (1996) Temple, TX 1–3 N Corn, sorghumSims et al. (1998) Clay Center, NE 3–5 Y CornSinger et al. (2004) Boone, IA 1–4 N Corn, soybeanSoon and Clayton (2002) Fort Vermillion, AB 1–8 N Winter wheatTarkalson et al. (2006) North Platte, NE 30–40 N Corn, winter wheatTarkalson et al. (2006) North Platte, NE 1–25 N SorghumTarkalson et al. (2006) North Platte, NE 3–20 N Winter wheatTerra et al. (2006) Shorter, AL 1–3 N CottonTorbert et al. (2001) Temple, TX 1–4 N CornTriplett et al. (1996) Starkville, MS 1–5 N CottonWest et al. (1996) Lafayette, IN 1–20 N Corn, soybeanWilhelm and Wortmann (2004) Lincoln, NE 1–16 N Corn, soybeanYin and Al-Kaisi (2004) Burlington, IA 13 N SoybeanYin and Al-Kaisi (2004) Nashua, IA 15 N SoybeanYin and Al-Kaisi (2004) Newell, IA 6 N SoybeanZentner et al. (2002) Indian Head, SK 11 N Spring wheat

4 tems a

vaat(c

ofatfSi

vpedaTawStcD

2

gCtt

�

wbsr0ewe

�

wig

�

a

2

rf(feSa

0 S.M. Ogle et al. / Agriculture, Ecosys

Most of the fixed effect variables were based on the data pro-ided in the publications, including N fertilization rates, organicmendment rates, irrigation, cover cropping practices, soil texturend hydric soil conditions. However, mean annual precipitation andemperature were based on long-term averages from the PRISMParameter-elevation Regressions on Independent Slopes Model)limate mapping system (Daly et al., 1994, 2008).

The models included latent random variables common to allbservations from the same experimental site and time seriesrom within a site. The random effects account for the dependencemong observations from the same site and time series, respec-ively, which is needed to estimate appropriate standard deviationsor the fixed effect parameters. All analyses were conducted withplus 7.0, Enterprise Version (Insightful Corporation, Seattle, Wash-ngton).

A county-scale map of the predicted change in yield with con-ersion from FT to NT was produced for each crop based on theredictions from the linear mixed-effect model. If none of the fixedffects were significant in the linear-mixed effect models, a confi-ence interval was produced for the mean of the samples assuming

Gaussian distribution and used to map the expected yield change.he resulting maps included all counties in the U.S. that hadt least 10,000 ha of the crop (or were surrounded by countiesith 10,000 ha of the crop) according to USDA-NASS Agricultural

tatistics (USDA-NASS, 2010). County-scale mean precipitation andemperature data were estimated using PRISM data for only theropland areas in each county based on the National Land Coverataset (Homer et al., 2007)

.3. Estimating carbon inputs

C inputs were estimated using a method adapted from the Inter-overnmental Panel on Climate Change (De Klein et al., 2006).hange in aboveground residue C (�CAR) with conversion from FTo NT was estimated using a linear regression model approach withhe following equation:

CAR = ˇ1�Y × C, (2)

here ˇ1 is the coefficient, �Y is the estimated yield differenceetween the NT and FT tillage systems (tonnes dry matter ha−1,ee Eq. (1)), and C is the proportion of carbon in the plant mate-ial (Note: the IPCC equation includes an intercept but this value is

in our application because the difference in residue C has beenstimated from �Y). The change in belowground residue C (�CBR)ith conversion from FT to NT was estimated using the following

quation:

CBR = (�CAR + Y) × R : S × C, (3)

here R:S is the root:shoot ratio (unitless). The total change in Cnput (CT) was estimated by summing the aboveground and below-round residue C amounts:

CT = �CAR + �CBR, (4)

nd the estimated change in C input is in tonnes C ha−1.

.4. Estimating soil organic C stocks

To evaluate the potential long-term impact of changes in cropesidue C inputs on SOC stocks, we used a steady-state solutionor the residue and SOM pools represented in the Century modelParton et al., 1987). In using an analytical (steady-state) solution

or the SOM sub-model in Century, our objective was to derive gen-ral relationships between residue C inputs, tillage and potentialOC stock changes as a function of regional variation in climatend soil texture. For this analysis, we used fixed parameter values

nd Environment 149 (2012) 37– 49

(i.e., decay and product partitioning constants, response functionsfor tillage disturbance, soil texture and litter quality) that had beenestimated from previous analyses of several long-term field exper-iments (e.g., Kelly et al., 1997; Parton et al., 1987, 1994; Paustianet al., 1996, 1997d).

The steady-state solution for total SOC in Century, as previouslyderived by Paustian et al. (1997a), is shown in Eq. (5),

X∗tot = I

{ˇ

k1+ (1 − ˇ)

k2

[1k3

+ f4k4

+ f5 + f4f6k5

]˛

+[

1k4

+ f6k5

]f3�(1 − ˇ)

}(5)

= f1 + [f2(1 − �) + f3�(f7 + f6f8)](1 − ˇ)(1 − f4f7 − f5f8 − f4f6f8)

(6)

where X∗tot is total SOC, which is the sum of the metabolic (X1), struc-

tural (X2), active (X3), slow (X4) and passive (X5) SOC pools; I is theC (residue) input rate; k1,2,3,4,5 are the specific decay rates for themetabolic, structural, active, slow and passive pools, respectively;f1 and f2 are the stabilization efficiencies for metabolic and struc-tural decay products, respectively, entering the active pool; f3 andf4 are the stabilization efficiencies for structural pool and activepool decay products, respectively, entering the slow pool; f5 andf6 are the stabilization efficiencies for active and slow pool decayproducts, respectively, entering the passive pool; f7 and f8 are thestabilization efficiencies for slow and passive pool decay products,respectively, entering the active pool; is the metabolic fraction ofresidue input (which is an empirical function of residue lignin (�)to nitrogen ratio); � is lignin content of residue; and � is the ligninto structural ratio of litter input [� = �/(1 − ˇ)].

Direct effects of tillage are represented in Century via two mech-anisms (Parton et al., 1987). The main effect is a transient increase inSOC pool decay rates due to physical soil disturbance (e.g. mixing,aggregate breakdown), which is represented by a multiplicativefactor (et) in the function for the specific decay rates (ki), i.e.,

ki = k′i · ec · et, (7)

where k′i

is the pool specific decay rate under optimum climaticconditions and no tillage disturbance, ec is the climate modifier(optimum = 1), et is the tillage disturbance modifier (no distur-bance = 1, values > 1 for increasing tillage intensity). The secondmechanism is a higher microbial yield efficiency for surface versusburied crop residues (i.e., f1 and f2 equal 0.45 for buried residuesand 0.55 for surface residues), based on shifts in microbial commu-nity structure towards greater fungal dominance in surface littercommunities under NT (Beare et al., 1992; Holland and Coleman,1987). The impact of both of these mechanisms is to decrease theMRT in FT compared to NT systems.

In addition to tillage, another main factor that determines SOCdecay rates in Century is climate, represented by maximum andminimum monthly temperature, monthly precipitation, potentialmonthly evapotranspiration (PET) and stored soil water, wherethe latter two variables are computed internally from the dynamicwater balance model in Century (Parton et al., 1987). Values fromthe temperature (tf) and moisture (wf) response functions (see Eq.(8) below) are combined to give the climate modifiers of the poten-tial decay rate parameters (k′

i) for each SOM pool (see Eq. (7)). Soil

texture also affects the specific decay rates (k3) and a stabilizationparameter (f4) associated with the active pool, where the specificfunctions are described in Parton et al. (1987). Poor soil drainage

(causing partially anaerobic condition) also affects decay rates inCentury, but was not considered in this analysis and so our resultsonly represent well-drained soils. Residue quality, expressed bylignin and N content, influences the partitioning of fresh residues

tems a

iiEsi

mmetwta2se

e

saslaMcuaftssew(

afwW

aaw(dtect5oddd

k

a

satebf

S.M. Ogle et al. / Agriculture, Ecosys

nto the structural versus metabolic pools and affects the partition-ng of structural decay products between active and slow pools (seeq. (5)). Lignin content also influences the specific decay rate of thetructural pool (i.e., specific decay rate decreases as lignin contentncreases, see Parton et al., 1987 for details).

To derive the climate factor (ec), we used gridded long-term cli-ate monthly means of maximum and minimum temperature andonthly precipitation, computed from the PRISM database (Daly

t al., 1994, 2008) and gridded monthly PET estimates based onhe Penman-Monteith equation (Vorosmarty et al., 1998), whichere averaged for croplands at the county-scale and used as inputs

o the temperature and moisture multiplicative factors. Croplandreas were based on the National Land Cover Dataset (Homer et al.,007). The overall climate factor was ‘annualized’ for the steady-tate equation by computing the joint temperature and moistureffects for each month and then averaging.

c =∑

tfi · wfi12

(8)

However, for the steady-state analysis we did not have monthlytored soil water contents as an input for estimation of wfi. Toccount for the effect of stored water on the climate factor in ourteady-state analysis, we estimated an adjustment factor by simu-ating several crops using Century for a range of climatic conditionss represented in the following states: Colorado, Georgia, Iowa,innesota, North Dakota, Nebraska, Ohio, Oregon, and Texas. We

ompared the values of the climate multiplier computed from sim-lations, which included the effect of stored water, precipitationnd PET, with the values using only precipitation and PET computedor the steady-state analysis. Across all climate types occurring inhe states, the value of the climate modifier when including storedoil water was increased by a factor of 0.502, with a reasonablymall standard deviation of 0.142. Given the similarity in the differ-nce between climate modifiers with or without including storedater, we used a single adjustment factor of +50% to compute ec

Eq. (8)) across all climate regions.To account for broad regional differences in soil texture we used

verage surface sand content by county. Sand content was derivedrom the gridded CONUS-SOIL database by calculating a depth-eighted average for sand fraction from 0 to 20 cm (Miller andhite, 1998).To represent the tillage effect on the decay constant, we set the

nnualized tillage disturbance factor, et, to 1.3 for the structural,ctive and slow pools, under FT, based on the tillage parameters thatere assessed for a large number of long-term tillage experiments

Ogle et al., 2005). For NT, et was set to 1 (i.e., no significant tillageisturbance) and the metabolic and passive pools were assumedo not be directly impacted by tillage disturbance. To simplify theffect of surface versus buried residues on the stabilization effi-iency of metabolic and structural decay products, we assumedhat 50% of total crop C inputs were added as surface residues (and0% belowground as root-derived material) under NT, while 100%f residues were treated as soil incorporated under FT, which pro-uced values for f1 and f2 of 0.5 for NT and 0.45 for FT. Thus theirect effects of tillage are expressed in the values of the specificecay constants,

i = k′i · ec · et, (9)

nd the values for the stabilization efficiencies f1 and f2.For any linear first-order decay model (e.g., dX/dt = I − kX), the

teady-state solution is X* = I/k, where k is the specific rate of decaynd I is input rate. For a system at steady-state, the mean residence

ime (MRT) of the pool is defined as 1/k. Based on Eq. (5), the brack-ted quantity on the right side of the equation, which is multipliedy the C input rate (I), gives the overall mean residence time (MRT)or total SOC (i.e., MRT = 1/k). Thus SOC stocks at steady-state (SOC*)

nd Environment 149 (2012) 37– 49 41

are directly proportional to both inputs and MRT, and a compressedform of Eq. (5) can be written as,

SOC∗FT = IFT · MRTFT (10a)

SOC∗NT = INT · MRTNT (10b)

Let �SOC represent the difference in SOC stocks between NTand FT systems and �I the difference in inputs between NT and FTsystems. Combining Eqs. (10a) and (10b),

�SOC = IFT(MRTNT − MRTFT) + �I · MRTNT, (11a)

and

�SOC > 0; if IFT(MRTNT − MRTFT) > �I · MRTNT (11b)

Under the mechanisms postulated in Century, MRT will begreater under NT compared with FT systems, all else being equal,and thus lower SOC would only be expected where a decrease inC input rates is sufficiently large to offset the impact of NT adop-tion on reduced rates of decay (i.e., increased MRT). For the pointat which this offset occurs (�SOC = 0), the relative change in theresidue input rate is directly proportional to the relative change inMRT due to tillage, i.e.,

−�I

IFT= MRTNT − MRTFT

MRTNT(12)

This means that independent of SOC stock level or the absolutelevel of C inputs, SOC stocks under NT would exceed those undertillage, as long as the percentage reduction in C inputs is less thanthe difference ratio of MRTs (Eq. (5)).

Changes in steady-state SOC levels due to changes in C inputrate and tillage were predicted at the county-scale. NASS-derivedresidue input amounts (averaged for 1980–2009) (USDA-NASS,2010) were used as the baseline rates for the FT system, and cropresidue inputs under NT were derived using the residue input dif-ferential (�CT) computed in Eq. (4). Annual residue inputs wereassumed except in the case of winter wheat, due to the prevalenceof winter wheat-fallow cropping systems in the western U.S. Wherecrop-fallow rotations occurred, average annual residue inputs fromwinter wheat were downward adjusted by the area of winter wheatfollowing fallow, relative to total area of winter wheat per year(USDA-NASS, 2010).

3. Results and discussion

3.1. Changes in yield

Linear mixed-effect models were developed for corn, soybeans,winter wheat and spring wheat (Table 2). Climatic conditions sig-nificantly influenced the response of corn (Fig. 1) and winter wheat(Fig. 2). According to the statistical models, corn yields are predictedto decline following conversion from FT to NT management in theNortheast, Mid-Atlantic, Great Lakes, Corn Belt and Great Plainswith low N fertilization rates (i.e., 50 kg N ha−1), and even withhigher N fertilization rates (i.e., 200 kg N ha−1), corn yields decreasein most of these regions except the Mid-Atlantic and southernGreat Plains. The largest reductions are in the Northeast, north-ern Corn Belt and Great Lakes regions where yield losses exceed0.5 tonnes C ha−1 with low N fertilization rates. Similar to corn,winter wheat is predicted to have the largest declines in yields fol-lowing NT adoption in the Corn Belt, Great Lakes and Mid-Atlanticregions. Climatic impacts are consistent with previous hypothesessuggesting lower yields in cooler and wetter climates (Dwyer et al.,

1995; Kitur et al., 1994). In contrast, NT adoption is predicted toincrease yields for corn and winter wheat in the southern portionof the U.S. Corn and winter wheat had the largest spatial distribu-tions, which may have contributed to the significance of the climate

42 S.M. Ogle et al. / Agriculture, Ecosystems and Environment 149 (2012) 37– 49

Table 2Comparison of beta coefficients and significance level among the crops included in the linear mixed-effect model analysis of yield change with conversion of FT to NT.Variables denoted with “NS” were not significant at an alpha level of 0.05. Note that only one first-order interaction was included in this table because no other interactionswere found to be significant.

Corn Soybeans Sorghum Winter wheat Spring wheat Cotton

Intercept NS NS NS 2.27 (0.05) −0.17 (<0.01) NSMean Annual Temperature (◦C) −0.02 (0.78) NS NS −0.16 (0.03) NS NSMean Annual Precipitation (mm) 0.002 (<0.01) NS NS −0.005 (0.03) NS NSN Fertilization Rate (kg ha−1) 0.002 (<0.01) NS NS NS 0.004 (<0.01) NSOrganic Amendment (kg ha−1) NS NS NS NS NS NSCover Crop (yes or no) NS NS NS NS NS NSIrrigation (yes or no) NS NS NS NS NS NSPesticides (yes or no) NS NS NS NS NS NSSoil Texture (Proportion of Sand) NS NS NS NS NS NSHydric Soil (yes or no) NS −0.24 (0.01) NS NS NS NSYears after Conversion to NT NS 0.02 (0.02) NS NS NS NSMean Annual Temperature x Mean Annual Precipitation 0.0001 (0.05) NS NS 0.0003 (0.02) NS NS

F per b2 es were

vg

bgaw

ig. 1. Predicted yield changes for corn (tonnes C ha−1 yr−1) with the lower and up00 kg N ha−1, and low N fertilization rates were estimated at 50 kg N ha−1; the ratxperimental data.

ariables, while studies of the other crops occurred within smallereographic regions with less climate variability.

Predictions of corn yield following NT adoption are influencedy the N fertilization rate, as noted above, with a larger loss or less

ain in yield due to lower N fertilization rates. A similar pattern waslso found for spring wheat (Fig. 3), which only had a loss in yieldith NT adoption at low N fertilization rates (34 kg N ha−1). This

Fig. 2. Predicted yield changes for winter wheat (tonnes C ha−1 yr−1) w

ound for the 95% confidence interval. High N fertilization rates were estimated ate based on the first and third quantile of N fertilization rate distribution from the

result is consistent with the hypothesis that nitrogen availability isreduced after NT adoption (Matowo et al., 1997), which would bemore critical with low N fertilization rates.

Soybeans were the third most widespread crop in our analysis,

and although climatic variation does not affect yields following NTadoption, yields are influenced by soil drainage according to thestatistical models (Fig. 4). Specifically, yields are estimated to

ith the lower and upper bound for the 95% confidence interval.

S.M. Ogle et al. / Agriculture, Ecosystems and Environment 149 (2012) 37– 49 43

Fig. 3. Predicted yield changes for spring wheat (tonnes C ha−1 yr−1) with the lower and upper bound for the 95% confidence interval. High N fertilization rates were estimatedat 67 kg N ha−1, and low N fertilization rates were estimated at 34 kg N ha−1; the rates were based on the first and third quantile of N fertilization rate distribution from theexperimental data.

Fig. 4. Predicted yield changes for soybeans (tonnes C ha−1 yr−1) with the lower and upper bound for the 95% confidence interval.

Fig. 5. Mean yield change for sorghum (tonnes C ha−1 yr−1) with the lower and upper bound for the 95% confidence interval.

44 S.M. Ogle et al. / Agriculture, Ecosystems and Environment 149 (2012) 37– 49

ith th

iioR

tvCaeoettiH

uFrtrnaoda

3

tmla1mtca(edtsIiadm

Fig. 6. Mean yield change for cotton (tonnes C ha−1 yr−1) w

ncrease on non-hydric soils, and decline on hydric soils, whichs consistent with the influence of wet soils on later emergencef seedlings leading to reduced crop growth (Iragavarapu andandall, 1995).

There were fewer studies evaluating the influence of NT adop-ion on sorghum and cotton yields, and we did not find significantariation in yield changes related to the variables that were tested.onsequently, yield changes were estimated based on the meannd standard error from the experimental data. According to thestimates, sorghum and cotton yields increase with NT adoptionn average (Figs. 5 and 6). These crops are common in the south-rn portion of U.S., and past studies have suggested that crops inhis region have increased production following NT adoption dueo reduced soil moisture losses and enhanced root growth frommproved soil structure (Jones and Popham, 1997; Kennedy andutchinson, 2001; Nyakatawa et al., 2000).

There is variability in the yield responses as represented in thepper and lower bounds of the 95% confidence intervals (Figs. 1–6).or example, yield changes with high N fertilization for corn canange from losses to gains in yield for most of the study region, withhe exception of the Great Lakes region. The high variation may beelated to annual weather variability that modifies the positive oregative impact of NT on crop yields; variation in the other man-gement practices that was not evaluated, such as pesticide use;r other variables that impact production, such as the incidence ofisease which can vary between NT and FT systems (Dill-Mackynd Jones, 2000).

.2. Changes in C inputs and soil organic C stocks

The overarching mechanistic influence of tillage in Century ishat physical disturbance of the soil from a tillage practice increases

icrobial activity and organic matter decomposition rates. This is aong-standing and widely held paradigm in soil science (e.g., Rovirand Greacen, 1957; Oades, 1984; Elliott, 1986; Elliott and Coleman,988; Reicosky et al., 1997; Paustian et al., 2000), although the exactechanisms involved are not fully understood. Research during

he past three decades has suggested that the physical disturbanceaused by tillage on soil structure and soil aggregate dynamics play

key mechanistic role in the tillage impact on decomposition ratese.g., Tisdall and Oades, 1982; Elliott, 1986; Beare et al., 1994; Sixt al., 1998, 2000, 2004). Evidence for shorter mean residence timeue to higher decomposition rates with tillage of soils, comparedo no-till management has been reported based on 13C analyses ofoil aggregate fractions (e.g., Paustian et al., 2000; Six et al., 2000).n our analysis, we explored the general relationship between C

nputs and losses via decomposition as a function of tillage man-gement at the regional scale. We used parameter values for SOCecay and stabilization processes derived from previous Centuryodel applications for several long-term field experiments (e.g.,

e lower and upper bound for the 95% confidence interval.

Kelly et al., 1997; Parton et al., 1987, 1994; Paustian et al., 1996,1997d).

The predicted changes in steady-state SOC stocks due to differ-ences in residue C inputs and SOM decay rates following a changein tillage are easily interpreted in terms of the basic mechanismspostulated in the model (Eq. (5)). However, interactions with otherfactors such as the basal rate of residue C inputs, climate influencesand soil texture combine to give a more complex picture whenviewed at the regional scale.

The MRTs for SOC in cropland soils varied by over an order ofmagnitude across the range of climate and soil texture classes in theU.S. (Fig. 7a and b), and the absolute differences in MRTs betweenNT and FT systems varied between 0.5 years and >10 years (Fig. 7c).The absolute largest differences in MRTs between tillage systemsoccurred in soils with low rates of decay (i.e., cool, dry) and highclay + silt content, where the values for MRTs were also the largest.Even though climate and texture greatly influenced the absolutedifferences in MRTs, relative differences in MRTs between NT andFT were almost constant at value of −0.15 (Fig. 7d). Therefore, NTincreases the MRT of SOC by approximately 15%, and so C inputwould need to decline by 15% or more to cause a reduction insteady-state SOC stocks.

The distribution and spatial pattern of steady-state SOC stocks isthe product of C input rates, which are driven by crop productivity,and MRT of SOC, which is driven mainly by climate and soil tex-ture (Fig. 8a). Higher stocks occur in areas where residue additionsare high and the intrinsic decomposition rates are low to moder-ate as reflected in the SOC MRT (Fig. 8b). In general, the higheststocks are predicted in the northern Corn Belt and the lowest in thesoutheastern U.S. The relatively high SOC stocks predicted in thewestern-central Great Plains reflect the high levels of productivityfor irrigated corn.

We evaluated the impact of predicted changes in C input withNT adoption on SOC stocks at the county-scale. Here we focus on theresults for corn and winter wheat, which are the dominant cropsin the region. These two crops had the greatest regional variabilityin yield responses among the crops that were analyzed, and thusa more complex pattern of responses. In addition, other crops didnot have a decline in yields below the 15% threshold, suggestingthat SOC would increase with NT adoption based on the C inputsalone.

As discussed above, corn yields are predicted to decreasewith NT adoption in the Northeast, Great Lakes, Corn Belt andnorthern Great Plains, regardless of fertilization rates, and Cinputs follow the same patterns (Fig. 9). However, except fora few counties, relative C inputs did not decrease by morethan the 15% threshold, and thus most of the corn-growing

regions are predicted to have an increase in SOC under NT fol-lowing conversion from FT management. In the northern partof the Great Lakes region, the model predicts little change in

S.M. Ogle et al. / Agriculture, Ecosystems and Environment 149 (2012) 37– 49 45

Fig. 7. Theoretical relationship between total SOC mean residence times (MRT) and climate (ec) and soil texture controls (expressed as mineral fraction as silt + clay)on decomposition rates, for (a) full tillage (FT), (b) no-tillage (NT), (c) absolute difference between NT and FT (i.e., MRTNT-MRTFT) and (d) relative difference in MRT([MRTFT − MRNT]/MRTNT).

Fig. 8. Predicted spatial distributions of (a) steady-state total SOC (tonnes C ha−1) and (b) MRT (years) under FT, using annual carbon input rates from corn, county soil textureand climate conditions.

46 S.M. Ogle et al. / Agriculture, Ecosystems and Environment 149 (2012) 37– 49

F state

a ct the

SlCAriM

Fta

ig. 9. Mean change in (a) corn residue C inputs (tonnes C ha−1 yr−1) and(b) steady-nd lower bound of the 95% confidence intervals. The upper and lower bounds refle

OC stocks as a function of tillage, while the largest abso-ute increases in SOC with NT adoption are predicted for theorn Belt, Mississippi River Valley and parts of the Great Plains.lthough corn is predicted to have higher yields and more

esidue C input under NT in the southeastern U.S., absolutencreases in steady-state SOC stocks are modest due to the short

RT.

ig. 10. Mean change in (a) winter wheat residue C inputs (tonnes C ha−1 yr−1) and (b) stehe upper and lower bound of the 95% confidence intervals. The upper and lower bounddoption of NT.

SOC stocks (tonnes C ha−1 yr−1) following conversion from FT to NT, with the upper uncertainty in carbon inputs due to corn yield changes with adoption of NT.

In contrast, C input from winter wheat residues under NT arepredicted to decrease in most of the Mid-Atlantic, central U.S. andin a few areas in the Pacific Northwest, and the relative decreasesare greater for wheat than corn (Fig. 10). Consequently, steady-state

SOC stocks in wheat production systems under NT are predicted tobe lower than FT in the Mid-Atlantic, northern Corn Belt and in partsof the Pacific Northwest, while SOC stocks under NT are predicted to

ady-state SOC stocks (tonnes C ha−1 yr−1) following conversion from FT to NT, withs reflect the uncertainty in carbon inputs due to winter wheat yield changes with

tems a

iU

tpddGtScaiNowS

ssiicaiwwfads

titfitot(bMmiiat(tcmdp

3

eifda1iwl

S.M. Ogle et al. / Agriculture, Ecosys

ncrease in the Northern Great Plains, southeast and south-central.S.

The upper and lower bounds of the 95% confidence intervals forhe estimated C inputs and SOC changes demonstrate a range ofotential responses (Figs. 9 and 10). In the extreme cases, the pre-ictions have an uncertainty range that includes both increases andecreases in steady-state SOC stocks, such as corn in the Northernreat Lakes region. In general, confidence intervals for corn tend

o include an upper and lower bound that suggests an increase inOC stocks for most regions regardless of the uncertainty. However,onfidence intervals for wheat are more likely to include both gainsnd losses of SOC, particularly in the Central U.S. The confidencentervals were based on variation in crop yield responses betweenT and FT management (Figs. 1 and 2). As discussed in the previ-us section, this variation may be due to interannual variability ineather, other management practices or variables influencing the

OC stocks in the experimental plots.Our analysis addresses potential long-term responses and

hould not be interpreted as assessing where and how much SOCtocks would change as a function of NT adoption. Most croplandsnvolve rotations of different crops, and thus impacts on residue Cnputs and SOC stocks over the rotation will be an aggregate of allrop responses to NT adoption. Similarly, we have not addressed

variety of other factors that would likely influence the response,ncluding previous land use history and time since NT adoption,

hich strongly affect SOC dynamics. In addition, variation existsithin NT field operations in terms of the level of soil disturbance

rom different seeders (Baker et al., 2007b). This variation may haven influence on SOC stocks, but was not evaluated in this analysisue to limited information on seeding operations in the publishedtudies.

Finally, processes other than C input rates and the decomposi-ion and stabilization mechanisms represented in Century may bemportant in determining the response of SOC stocks to changes inillage. Our analysis did not represent explicit depths in a soil pro-le but rather a single homogenized soil with decomposition ratesypical of surface horizons. It has been suggested that incorporationf residues with FT can increase residue-derived C inputs belowhe depth of tillage through leaching and/or dispersive processesGregorich et al., 2009) and/or that C allocation to root growthelow the plow layer may be greater under FT (Baker et al., 2007a).oreover, residue decay rates, particularly in cool, moist environ-ents may be slower at depth in subsurface horizons, which would

ncrease the MRT of SOC derived from residues incorporated deepern the soil (Olchin et al., 2008). In addition, where surface soils arelready rich in organic matter, there is evidence of saturation ofhe organic matter stabilization capacity of soil mineral fractionsGregorich et al., 2009; Stewart et al., 2008). In these situations, theillage-enhanced movement of C to deeper soil layers, where SOMoncentrations are lower and the SOM saturation deficit is greater,ay compensate for the higher rates of SOC loss due to aggregate

isruption in the plow layer (Six et al., 2000), resulting in equal orerhaps greater overall gains in SOC for the FT systems.

.3. Conclusions

Adoption of NT can increase or decrease yields depending onnvironmental conditions, and in turn, the change in yield willmpact residue C inputs to soils. Using standard parameterizationor the Century model, our analysis suggests that where C inputsecline by more than 15%, then SOC stocks would also decline withdoption of NT, and that where C inputs decrease by less than

5% (or C inputs increase), then SOC stocks would be expected to

ncrease. Consequently, a reduction in residue C inputs under NT,here they occur, does provide a mechanistic explanation for a

ack of increase in SOC with NT adoption, and therefore NT will not

nd Environment 149 (2012) 37– 49 47

always serve to mitigate greenhouse gas emissions, which has beennoted by others (Baker et al., 2007a).

Other mechanisms influencing SOM dynamics such as satura-tion limits for SOM stabilization in organo-mineral complexes, mayalso be an explanation for a reduction in SOC with NT adoption, par-ticularly in cool, moist environments where surface organic matterconcentrations tend to be high. Likewise, the influence of varia-tion in NT seeding methods on soil disturbance, as well as residueincorporation and/or root growth deeper in subsurface horizonswhere decomposition is slower may also influence the net changein SOC stocks between FT and NT management systems. Incorpo-rating these processes into analyses along with changes in C inputsand aggregate structure represent key future challenges to morefully understand and precisely predict the impact of NT adoptionon C sequestration in soils.

Acknowledgements

Thanks for technical support from Steve Williams and RamGurung, as well as statistical advice provided by F. Jay Breidt. Thisresearch was supported by the USDA National Research Initiativeof the Cooperative State Research, Education and Extension Service(Grant # 2005-35400-15294).

References

Aflakpui, G.K.S., Vyn, T.J., Anderson, G.W., Clements, D.R., Hall, M.R., Swanton, C.J.,1994. Crop management systems for corn (Zea mays L.) following establishedalfalfa (Medicago sativa L.). Canadian Journal of Plant Science 74, 255–259.

Akiake, H., 1973. Information theory and an extension of the maximum likelihoodprinciple. In: Second International Symposium on Information Theory, Tsahkad-sor, Armenian SSR, Hungary, pp. 267–281.

Al-Kaisi, M.M., Yin, X., Licht, M.A., 2005. Soil carbon and nitrogen changes affected bytillage system and crop biomass in a corn–soybean rotation. Applied Soil Ecology30, 174–191.

Anders, M.M., Windham, T.E., McNew, R.W., Reinecke, K.J., 2005. Fall rice straw man-agement and winter flooding treatment effects on a subsequent soybean crop.Journal of Sustainable Agriculture 26, 83–95.

Angers, D.A., Bolinder, M.A., Carter, M.R., Gregorich, E.G., Drury, C.F., Liang, B.C.,Voroney, R.P., Simard, R.R., Donald, R.G., Beyaert, R.P., Martel, J., 1997. Impactof tillage practices on organic carbon and nitrogen storage in cool, humid soilsof eastern Canada. Soil and Tillage Research 41, 191–201.

Baker, J.M., Ochsner, T.E., Venterea, R.T., Griffis, T.J., 2007a. Tillage and soil carbonsequestration – what do we really know? Agriculture, Ecosystems & Environ-ment 118, 1–5.

Baker, C.J., Saxton, K.E., Ritchie, W.R., Chamen, W.C.T., Reicosky, D.C., Ribeiro, M.F.S.,Justice, S.E., Hobbs, P.R., 2007b. No-tillage Seeding in Conservation Agriculture,2nd ed. FAO and CAB International, Cromwell Press, Trowbridge, United King-dom.

Beare, M.H., Parmelee, R.W., Hendrix, P.F., Cheng, W.X., Coleman, D.C., Crossley,D.A., 1992. Microbial and faunal interactions and effects on litter nitrogen anddecomposition in agroecosystem. Ecological Monographs 62, 569–591.

Beare, M.H., Hendrix, P.F., Coleman, D.C., 1994. Water-stable aggregates and organicmatter fractions in conventional- and no-tillage soils. Soil Science Society ofAmerica Journal 58, 777–786.

Beyaert, R.P., Schott, J.W., White, P.H., 2002. Tillage effects on corn production incoarse-textured soil in southern Ontario. Agronomy Journal 94, 767–774.

Blanco-Canqui, H., Lal, R., 2008. No-tillage and soil-profile carbon sequestration: anon-farm assessment. Soil Science Society of America Journal 72, 693–701.

Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multi-model Inference: APractical Information-theoretic Approach, 2nd ed. Springer, New York, 448 pp.

Cassel, D.K., Wagger, M.G., 1996. Residue management for irrigated maize grain andsilage production. Soil and Tillage Research 39, 101–114.

Christopher, S.F., Lal, R., Mishra, U., 2009. Regional study of no-till effects on carbonsequestration in the midwestern United States. Soil Science Society of AmericaJournal 73, 207–216.

Cox, W.J., Otis, D.J., Van Es, H.M., Gaffney, F.B., Snyder, D.P., Reynolds, K.R., Vander Grinten, M., 1992. Feasibility of no-tillage and ridge tillage systems in thenortheastern USA. Journal of Production Agriculture 5, 111–117.

Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J.,Pasteris, P.P., 2008. Physiographically sensitive mapping of climatological tem-perature and precipitation across the conterminous United States. InternationalJournal of Climatology 28, 2031–2064.

Daly, C., Neilson, R.P., Phillips, D.L., 1994. A statistical-topographic model for map-ping climatological precipitation over mountainous terrain. Journal of AppliedMeteorology 33, 140–158.

Dam, R.F., Mehdi, B.B., Burgess, M.S.E., Madramootoo, C.A., Mehuys, G.R., Callum,I.R., 2005. Soil bulk density and crop yield under eleven consecutive years of

4 tems a

D

D

D

D

D

D

D

D

D

E

E

E

E

E

F

F

G

G

G

G

H

H

H

H

H

H

H

H

H

8 S.M. Ogle et al. / Agriculture, Ecosys

corn with different tillage and residue practices in a sandy loam soil in centralCanada. Soil and Tillage Research 84, 41–53.

e Klein, C., Novoa, R.S.A., Ogle, S., Smith, K.A., Rochette, P., Wirth, T.C., 2006. N2Oemissions from managed soils, and CO2 emissions from lime and urea applica-tion. In: Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.), 2006IPCC Guidelines for National Greenhouse Gas Inventories. Institute for GlobalEnvironmental Strategies, Hyama, Japan.

iaz-Zorita, M., Grove, J.H., Murdock, L., Herbeck, J., Perfect, E., 2004. Soil structuraldisturbance effects on crop yields and soil properties in a no-till productionsystem. Agronomy Journal 96, 1651–1659.

ick, W.A., Van Doren Jr, D.M., 1985. Continuous tillage and rotation com-binations effects on corn, soybean, and oat yields. Agronomy Journal 77,459–465.

ickey, E.C., Siemens, J.C., Jasa, P.J., Hofman, V.L., Shelton, D.P., 1992. Tillage sys-tem definitions. In: MidWest Service Plan, Conservation Tillage Systems andManagement: Crop Residue Management with No-till, Ridge-till, Mulch-till.MidWest Plan Service, Iowa State University, Ames, IA, USA, pp. 5–7.

ill-Macky, R., Jones, R.K., 2000. The effect of previous crop residues and tillage onFusarium head blight of wheat. Plant Disease 84, 71–76.

oran, J.W., Elliott, E.T., Paustian, K., 1998. Soil microbial activity, nitrogen cycling,and long-term changes in organic carbon pools as related to fallow tillage man-agement. Soil and Tillage Research 49, 3–18.

rinkwater, L.E., Janke, R.R., Rossoni-Longnecker, L., 2000. Effects of tillage intensityon nitrogen dynamics and productivity in legume-based grain systems. Plantand Soil 227, 99–113.

rury, C.F., Tan, C.S., Reynolds, W.D., Welacky, T.W., Weaver, S.E., Hamill, A.S.,Vyn, T.J., 2003. Impacts of zone tillage and red clover on corn perfor-mance and soil physical quality. Soil Science Society of America Journal 67,867–877.

wyer, L.M., Ma, B.L., Hayhoe, H.N., Culley, J.L.B., 1995. Tillage effects on soil temper-ature, shoot dry matter accumulation and corn grain yield. Journal of SustainableAgriculture 5, 85–99.

dwards, J.H., Thurlow, D.L., Eason, J.T., 1988. Influence of tillage and crop rotationon yields of corn, soybean, and wheat. Agronomy Journal 80, 76–80.

ghball, B., Power, J.F., 1999. Composted and noncomposted manure application toconventional and no-tillage systems: corn yield and nitrogen uptake. AgronomyJournal 91, 819–825.

lliott, E.T., 1986. Aggregate structure and carbon, nitrogen, and phosphorusin native and cultivated soils. Soil Science Society of America Journal 50,627–633.

lliott, E.T., Coleman, D.C., 1988. Let the soil work for us. Ecological Bulletins 39,23–32.

pplin, F.M., Al-Sakkaf, G.A., Peeper, T.F., 1994. Impacts of alternative tillage methodsfor continuous wheat on grain yield and economics: implications for conserva-tion compliance. Journal of Soil and Water Conservation 49, 394–399.

ollett, R.F., 2001. Soil management concepts and carbon sequestration in croplandsoils. Soil and Tillage Research 61, 77–92.

ranzluebbers, A.J., 2002. Soil organic matter stratification ratio as an indicator ofsoil quality. Soil and Tillage Research 66, 95–106.

regorich, E.G., Carter, M.R., Angers, D.A., Drury, C.F., 2009. Using a sequential den-sity and particle-size fractionation to evaluate carbon and nitrogen storage inthe profile of tilled and no-till soils in eastern Canada. Canadian Journal of SoilScience 89, 255–267.

riffith, D.R., Kladivko, E.J., Mannering, J.V., West, T.D., Parsons, S.D., 1988. Long-termtillage and rotation effects on corn growth and yield on high and low organicmatter, poorly drained soils. Agronomy Journal 80, 599–605.

upta, S.C., 1985. Predicting corn planting dates for moldboard and no-till tillagesystems in the corn belt. Agronomy Journal 77, 446–455.

uy, S.O., Cox, D.B., 2002. Reduced tillage increases residue groundcover in subse-quent dry pea and winter wheat crops in the Palouse region of Idaho. Soil andTillage Research 66, 69–77.

airston, J.E., Jones, W.F., McConnaughey, P.K., Marshall, L.K., Gill, K.B., 1990. Tillageand fertilizer management effects on soybean growth and yield on three Mis-sissippi soils. Journal of Production Agriculture 3, 317–323.

alvorson, A.D., Black, A.L., Krupinsky, J.M., Merrill, S.D., 1999. Dryland winter wheatresponse to tillage and nitrogen within an annual cropping system. AgronomyJournal 91, 702–707.

alvorson, A.D., Wienhold, B.J., Black, A.L., 2002. Tillage, nitrogen, and cropping sys-tem effects on soil carbon sequestration. Soil Science Society of America Journal66, 906–912.

ammel, J.E., 1995. Long-term tillage and crop rotation effects on winter wheatproduction in northern Idaho. Agronomy Journal 87, 16–22.

argrove, W.L., 1985. Influence of tillage on nutrient uptake and yield of corn. Agron-omy Journal 77, 763–768.

olland, E.A., Coleman, D.C., 1987. Litter placement effects on microbial and organic-matter dynamics in an agroecosystem. Ecology 68, 425–433.

omer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow,A., VanDriel, J.N., Wickam, J., 2007. Completion of the 2001 National Land CoverDatabase for the Conterminous United States. Photogrammetric Engineeringand Remote Sensing 73, 337–341.

unt, P.G., Bauer, P.J., Matheny, T.A., Busscher, W.J., 2004. Crop yield and nitro-

gen accumulation response to tillage of a Coastal Plain soil. Crop Science 44,1673–1681.

ussain, I., Olson, K.R., Ebelhar, S.A., 1999. Impacts of tillage and no-till on productionof maize and soybean on an eroded Illinois silt loam soil. Soil and Tillage Research52, 37–49.

nd Environment 149 (2012) 37– 49

Iragavarapu, T.K., Randall, G.W., 1995. Yield and nitrogen uptake of monocroppedmaize from a long-term tillage experiment on a poorly drained soil. Soil andTillage Research 34, 145–156.

Ismail, I., Blevins, R.L., Frye, W.W., 1994. Long-term no-tillage effects on soil prop-erties and continuous corn yields. Soil Science Society of America Journal 58,193–198.

Jastrow, J.D., Boutton, T.W., Miller, R.M., 1996. Carbon dynamics of aggregate-associated organic matter estimated by carbon-13 natural abundance. SoilScience Society of America Journal 60, 801–807.

Jensen, C.E.d., Kurle, J.E., Percich, J.A., 2004. Integrated management of edaphic andbiotic factors limiting yield of irrigated soybean and dry bean in Minnesota. FieldCrops Research 86, 211–224.

Johnson, W.C., Brenneman, T.B., Baker, S.H., Johnson, A.W., Sumner, D.R.B.G.,Mullinix, J., 2001. Tillage and pest management considerations in apeanut–cotton rotation in the southeastern coastal plain. Agronomy Journal 93,570–576.

Jones, O.R., Popham, T.W., 1997. Cropping and tillage systems for dryland grainproduction in the southern High Plains. Agronomy Journal 89, 222–232.

Karlen, D.L., Berry, E.C., Colvin, T.S., 1991. Twelve-year tillage and crop rotationeffects on yields and soil chemical properties in northeast-Iowa. Communica-tions in Soil Science and Plant Analysis 22, 1985–2003.

Karlen, D.L., Hunt, P.G., Matheny, T.A., 1996. Fertilizer 15 Nitrogen recovery by corn,wheat and cotton grown with and without pre-plant tillage on Norfolk loamysand. Crop Science 36, 975–981.

Karunatilake, U., van Es, H.M., Schindelbeck, R.R., 2000. Soil and maize response toplow and no-tillage after alfalfa-to-maize conversion on a clay loam soil in NewYork. Soil and Tillage Research 55, 31–42.

Kelly, R.H., Parton, W.J., Crocker, G.J., Grace, P.R., Klír, J., Körchens, M., Poulton, P.R.,Richter, D.D., 1997. Simulating trends in soil organic carbon in long-term exper-iments using the Century model. Geoderma 81, 75–90.

Kennedy, C.W., Hutchinson, R.L., 2001. Cotton growth and development under dif-ferent tillage systems. Crop Science 41, 1162–1168.

Kessavalou, A., Walters, D.T., 1997. Winter rye as a cover crop following soybeanunder conservation tillage. Agronomy Journal 89, 68–74.

Kettler, T.A., Lyon, D.J., Doran, J.W., Powers, W.L., Stroup, W.W., 2000. Soil qualityassessment after weed-control tillage in a no-till wheat-fallow cropping system.Soil Science Society of America Journal 64, 339–346.

Kitur, B.K., Olson, K.R., Ebelhar, S.A., Bullock, D.G., 1994. Tillage effects on growthand yields of corn on Grantsburg soil. Journal of Soil and Water Conservation 49,266–274.

Koenning, S.R., Schmitt, D.P., Barker, K.R., Gumpertz, M.L., 1995. Impact of crop rota-tion and tillage system on Heterodera glycines population density and soybeanyield. Plant Disease 79, 282–286.

Kurle, J.E., Gran, C.R., Oplinger, E.S., Mengistu, A., 2001. Tillage, crop sequence, andcultivar effects on Sclerotinia stem rot incidence and yield in soybean. AgronomyJournal 93, 973–982.

Lal, R., 1996. Axle load and tillage effects on crop yields on a Mollic Ochraqualf innorthwest Ohio. Soil and Tillage Research 37, 143–160.

Lal, R., Kimble, J.M., Follett, R.F., Cole, C.V., 1998. The Potential of U.S. Cropland toSequester Carbon and Mitigate the Greenhouse Effect. Sleeping Bear Press, Inc.,Chelsea, MI.

Larney, F.J., Lindwall, C.W., 1994. Winter-wheat performance in various croppingsystems in southern Alberta. Canadian Journal of Plant Science 74, 79–86.

Linden, D.R., Clapp, C.E., Dowdy, R.H., 2000. Long-term corn grain and stover yieldsas a function of tillage and residue removal in east central Minnesota. Soil andTillage Research 56, 167–174.

Lindwall, C.W., Larney, F.J., Carefoot, J.M., 1995. Rotation, tillage, and seeder effectson winter wheat performance and soil moisture regime. Canadian Journal of SoilScience 75, 109–116.

Liu, W.D., Tollenaar, M., Stewart, G., Deen, W., 2004. Impact of planter type, plantingspeed, and tillage on stand uniformity and yield of corn. Agronomy Journal 96,1668–1672.

Lowery, B., Hartwig, R.C., Stoltenberg, D.E., Fermanich, K.J., McSweeney, K., 1998.Groundwater quality and crop-yield responses to tillage management on aSparta sand. Soil and Tillage Research 48, 225–237.

Lyon, D.J., Stroup, W.W., Brown, R.E., 1998. Crop production and soil water storagein long-term winter wheat-fallow tillage experiments. Soil and Tillage Research49, 19–27.

Matowo, P.R., Pierzynski, G.M., Whitney, D.A., Lamond, R.E., 1997. Long term effectsof tillage and nitrogen source, rate, and placement on grain sorghum production.Journal of Production Agriculture 10, 141–146.

McAndrew, D.W., Fuller, L.G., Wetter, L.E., 1994. Grain and straw yields of barleyunder four tillage systems in northeastern Alberta. Canadian Journal of PlantScience 74, 713–722.

McConkey, B.G., Campbell, C.A., Zentner, R.P., Dyck, F.B., Selles, F., 1996. Long-termtillage effects on spring wheat production on three soil textures in the Brownsoil zone. Canadian Journal of Plant Science 76, 747–756.

Miller, D.A., White, R.A., 1998. A conterminous United States multi-layer soilcharacteristics data set for regional climate and hydrological modeling. EarthInteractions 2, 1–26.

Moschler, W.W., Shear, G.M., Martens, D.C., Jones, G.D., Wilmouth, R.R., 1972. Com-

parative yield and fertilizer efficiency of no-tillage and conventionally tilledcorn. Agronomy Journal, 229–231.

Mueller, D.S., Hartman, G.L., Pedersen, W.L., 2002. Effect of crop rotation and tillagesystem on sclerotinia stem rot on soybean. Canadian Journal of Plant Pathology24, 450–456.

tems a

N

N

O

O

O

O

P

P

P

P

P

P

P

P

P

P

R

R

S

S

S.M. Ogle et al. / Agriculture, Ecosys

orwood, C., 1994. Profile water distribution and grain yield as affected by croppingsystem and tillage. Agronomy Journal 86, 558–563.

yakatawa, E.Z., Reddy, K.C., Mays, D.A., 2000. Tillage, cover cropping, and poultrylitter effects on cotton: II. Growth and yield parameters. Agronomy Journal 92,1000–1007.

ades, J.M., 1984. Soil organic matter and structural stability: mechanisms andimplications for management. Plant and Soil 76, 319–337.

gle, S.M., Breidt, F.J., Paustian, K., 2005. Agricultural management impacts on soilorganic carbon storage under moist and dry climatic conditions of temperateand tropical regions. Biogeochemistry 72, 87–121.

lchin, G.P., Ogle, S., Frey, S.D., Filley, T.R., Paustian, K., Six, J., 2008. Residue car-bon stabilization in soil aggregates of no-till and tillage management of drylandcropping systems. Soil Science Society of America Journal 72, 507–513.

poku, G., Vyn, T.J., Swanton, C.J., 1997. Modified no-till systems for corn followingwheat on clay soils. Agronomy Journal 89, 549–556.

arton, W.J., Schimel, D.S., Cole, C.V., Ojima, D.S., 1987. Analysis of factors control-ling soil organic matter dynamics in an agroecosystem. Soil Science Society ofAmerica Journal 51, 1173–1179.

arton, W.J., Ojima, D.S., Cole, C.V., Schimel, D.S., 1994. A general model for soilorganic matter dynamics: sensitivity to litter chemistry, texture and manage-ment. In: Quantitative Modeling of Soil Forming Processes. Special Publication39. SSSA, Madison, WI, pp. 147–167.

austian, K., Elliott, E.T., Peterson, G.A., Killian, K., 1996. Modelling climate, CO2 andmanagement impacts on soil carbon in semi-arid agroecosystems. Plant and Soil187, 351–365.

austian, K., Agren, G., Bosatta, E., 1997a. Modeling litter quality effects on decompo-sition and soil organic matter dynamics. In: Cadisch, G., Giller, K.E. (Eds.), Drivenby Nature: Plant Litter Quality and Decomposition. CAB International, UK, pp.316–336.

austian, K., Andren, O., Janzen, H.H., Lal, R., Smith, P., Tian, G., Tiessen, H., Noordwijk,M.V., Woomer, P.L., 1997b. Agricultural soils as a sink to mitigate CO2 emissions.Soil Use and Management 13, 230–244.

austian, K., Collins, H.P., Paul, E.A., 1997c. Management controls on soil carbon. In:Paul, E.A., Elliot, T.E., Paustian, K., Cole, C.V. (Eds.), Soil Organic Matter in Tem-perate Agroecosystems: Long-Term Experiments in North America. CRC Press,Boca Raton, pp. 15–49.

austian, K., Elliott, E.T., Killian, K., 1997d. Modeling soil carbon in relation to man-agement and climate change in some agroecosystems in central North America.In: Lal, R., Kimble, J.M., Follett, R.F., Stewart, B.A. (Eds.), Soil Processes and theCarbon Cycle. CRC Press, Boca Raton, FL, USA, pp. 459–471.

austian, K., Elliott, E.T., Six, J., Hunt, H.W., 2000. Management options for reducingCO2 emissions from agricultural soils. Biogeochemistry 48, 147–163.

inheiro, J.C., Bates, D.M., 2000. Mixed-effect Models in S and S-Plus. Springer, NewYork, 528 pp.

otter, K.N., Morrison, J.E., Torbert, H.A., 1996. Tillage intensity effects on corn andgrain sorghum growth and productivity on a Vertisol. Journal of ProductionAgriculture 9, 385–390.

eicosky, D.C., Dugas, W.A., Tolbert, H.A., 1997. Tillage-induced soil carbon dioxideloss from different cropping systems. Soil and Tillage Research 41, 105–118.

ovira, A.D., Greacen, E.L., 1957. The effect of aggregate disruption on the activ-ity of microorganisms in the soil. Australian Journal of Agricultural Research 8,659–673.

ims, A.L., Schepers, J.S., Olson, R.A., Power, J.F., 1998. Irrigated corn yield and nitro-

gen accumulation response in a comparison of no-till and conventional till:tillage and surface-residue variables. Agronomy Journal 90, 630–637.

inger, J.W., Kohler, K.A., Liebman, M., Richard, T.L., Cambardella, C.A., Buhler, D.D.,2004. Tillage and compost affect yield of corn, soybean, and wheat and soilfertility. Agronomy Journal 96, 531–537.

nd Environment 149 (2012) 37– 49 49

Six, J., Elliott, E.T., Paustian, K., 2000. Soil macroaggregate turnover and microaggre-gate formation: a mechanism for C sequestration under no-tillage agriculture.Soil Biology and Biochemistry 32, 2099–2103.

Six, J., Elliott, E.T., Paustian, K., Doran, J.W., 1998. Aggregation and soil organic matteraccumulation in cultivated and native grassland soils. Soil Science Society ofAmerica Journal 62, 1367–1377.

Six, J., Bossuyt, H., Degryze, S., Denef, K., 2004. A history of research on the linkbetween (micro)aggregates, soil biota, and soil organic matter dynamics. Soiland Tillage Research 79, 7–31.

Soon, Y.K., Clayton, G.W., 2002. Eight years of crop rotation and tillage effectson crop production and N fertilizer use. Canadian Journal of Soil Science 82,165–172.

Stewart, C.E., Plante, A.F., Paustian, K., Conant, R.T., Six, J., 2008. Soil carbon sat-uration: linking concept and measurable carbon pools. Soil Science Society ofAmerica Journal 72, 379–392.

Tarkalson, D.D., Hergert, G.W., Cassman, K.G., 2006. Long-term effects of tillageon soil chemical properties and grain yields of a dryland winter wheat-sorghum/corn-fallow rotation in the Great Plains. Agronomy Journal 98,26–33.

Terra, J.A., Shaw, J.N., Reeves, D.W., Raper, R.L., van Santen, E., Schwab, E.B., Mask,P.L., 2006. Soil management and landscape variability affects field-scale cottonproductivity. Soil Science Society of America Journal 70, 98–107.

Tisdall, J.M., Oades, J.M., 1982. Organic matter and water-stable aggregates in soils.Journal of Soil Science 33, 141–163.

Torbert, H.A., Potter, K.N., Morrison, J.E., 2001. Tillage system, fertilizer nitrogen rate,and timing effect on corn yields in the Texas Blackland Prairie. Agronomy Journal93, 1119–1124.

Triplett, G.B., Dabney, S.M., Siefker, J.H., 1996. Tillage systems for cotton on siltyupland soils. Agronomy Journal 88, 507–512.

USDA-NASS, 2010. County Data – Crops, 1980–2009. US Department of Agriculture,National Agriculture Statistics Service, Washington, DC.

USDA-NRCS, 2010. National Handbook of Conservation Practices, Conservation Prac-tice Standard, Residue and Tillage Management No Till/Strip Till/Direct Seed.United States Department of Agriculture, Natural Resources Conservation Ser-vice, Washington, DC.

Vorosmarty, C.J., Federer, C.A., Schloss, A.L., 1998. Potential evapotranspiration func-tions compared on US watersheds: possible implications for global-scale waterbalance and terrestrial ecosystem modeling. Journal of Hydrology 207, 147–169.

West, T.D., Griffith, D.R., Steinhardt, G.C., Kladivko, E.J., Parsons, S.D., 1996. Effect oftillage and rotation on agronomic performance of corn and soybean: twenty-year study on dark silty clay loam soil. Journal of Production Agriculture 9,241–248.

West, T.O., Post, W.M., 2002. Soil organic carbon sequestration rates by tillage andcrop rotation: a global data analysis. Soil Science Society of America Journal 66,1930–1946.

Wilhelm, W.W., Wortmann, C.S., 2004. Tillage and rotation interactions for corn andsoybean grain yield as affected by precipitation and air temperature. AgronomyJournal 96, 425–432.

Yang, X.-M., Wander, M.M., 1999. Tillage effects on soil organic carbon distributionand storage in a silt loam soil in Illinois. Soil and Tillage Research 52, 1–9.

Yin, X., Al-Kaisi, M.M., 2004. Periodic response of soybean yields and economicreturns to long-term no-tillage. Agronomy Journal 96, 723–733.

Young, H.M., 1982. No-tillage Farming. No-Till Farmer, Inc., Brookfield, WI, USA, pp.

27–29.

Zentner, R.P., Lafond, G.P., Derksen, D.A., Campbell, C.A., 2002. Tillage method andcrop diversification: effect on economic returns and riskness of cropping sys-tems in a Thin Black Chernozem of the Canadian Prairies. Soil and TillageResearch 67, 9–21.