RESEARCH ARTICLE
A Global Meta-Analysis on the Impact of
Management Practices on Net Global
Warming Potential and Greenhouse Gas
Intensity from Cropland Soils
Upendra M. Sainju*
USDA, Agricultural Research Service, Northern Plain Agricultural Research Laboratory, 1500 North Central
Avenue, Sidney, MT 59270, United States of America
* upendra.sainju@ars.usda.gov
Abstract
OPEN ACCESS
Citation: Sainju UM (2016) A Global Meta-Analysis
on the Impact of Management Practices on Net
Global Warming Potential and Greenhouse Gas
Intensity from Cropland Soils. PLoS ONE 11(2):
e0148527. doi:10.1371/journal.pone.0148527
Editor: Shuijin Hu, North Carolina State University,
UNITED STATES
Received: August 28, 2015
Accepted: January 16, 2016
Published: February 22, 2016
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced, distributed,
transmitted, modified, built upon, or otherwise used
by anyone for any lawful purpose. The work is made
available under the Creative Commons CC0 public
domain dedication..
Data Availability Statement: All relevant data are
within the paper.
Funding: The authors have no support or funding to
report.
Competing Interests: The author is also an
academic editor of PLOS ONE. This does not alter
the author's adherence to PLOS ONE policies on
sharing data and materials.
Abbreviations: GHG, greenhouse gas; GHGI,
greenhouse gas intensity; GWP, global warming
potential; SOC, soil organic C; ΔSOC, soil C
sequestration rate.
Management practices, such as tillage, crop rotation, and N fertilization, may affect net
global warming potential (GWP) and greenhouse gas intensity (GHGI), but their global
impact on cropland soils under different soil and climatic conditions need further evaluation.
Available global data from 57 experiments and 225 treatments were evaluated for individual
and combined effects of tillage, cropping systems, and N fertilization rates on GWP and
GHGI which accounted for CO2 equivalents from N2O and CH4 emissions with or without
equivalents from soil C sequestration rate (ΔSOC), farm operations, and N fertilization. The
GWP and GHGI were 66 to 71% lower with no-till than conventional till and 168 to 215%
lower with perennial than annual cropping systems, but 41 to 46% greater with crop rotation
than monocroppping. With no-till vs. conventional till, GWP and GHGI were 2.6- to 7.4-fold
lower when partial than full accounting of all sources and sinks of greenhouse gases
(GHGs) were considered. With 100 kg N ha-1, GWP and GHGI were 3.2 to 11.4 times
greater with partial than full accounting. Both GWP and GHGI increased curvilinearly with
increased N fertilization rate. Net GWP and GHGI were 70 to 87% lower in the improved
combined management that included no-till, crop rotation/perennial crop, and reduced N
rate than the traditional combined management that included conventional till, monocopping/annual crop, and recommended N rate. An alternative soil respiration method, which
replaces ΔSOC by soil respiration and crop residue returned to soil in the previous year,
similarly reduced GWP and GHGI by 133 to 158% in the improved vs. the traditional combined management. Changes in GWP and GHGI due to improved vs. traditional management varied with the duration of the experiment and inclusion of soil and climatic factors in
multiple linear regressions improved their relationships. Improved management practices
reduced GWP and GHGI compared with traditional management practices and combined
management practices were even more effective than individual management practices in
reducing net GHG emissions from cropland soils. Partial accounting overestimated GWP
and GHGI values as sinks or sources of net GHGs compared with full accounting when
evaluating the effect of management practices.
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Introduction
Agricultural management practices contribute from 6% of the total greenhouse gas (GHGs: CO2,
N2O, and CH4) emissions in the USA [1] to about 20% globally [2]. The impact of these GHGs in
radiative forcing in earth’s atmosphere is quantitatively estimated by calculating net global warming potential (GWP) which accounts for all sources and sinks of CO2 equivalents from farm operations, chemical inputs, soil C sequestration, and N2O and CH4 emissions [3, 4]. Another measure
of GHGs’ impact is net greenhouse gas intensity (GHGI) which is expressed as net GWP per unit
crop yield [4]. While soil C sequestration is the major sink and GHG emissions are sources of CO2
in agroecosystems that are affected by soil and climatic conditions and management practices [4,
5], machinery and inputs used for growing crops, such as tillage, planting, harvesting, and applications of fertilizers, herbicides, and pesticides, can produce CO2, thereby reducing the GHG mitigation potential [3, 6, 7]. The GWP and GHGI are typically controlled by the balance between soil C
sequestration rate (ΔSOC), N2O and CH4 emissions, and crop yields [3, 4, 8].
Novel management practices that can mitigate GHG emissions and therefore GWP and
GHGI include no-till, increased cropping intensity, diversified crop rotation, cover cropping,
and reduced N fertilization rates [3, 4, 8]. No-till can increase soil organic C (SOC) compared
with conventional till by reducing soil disturbance, residue incorporation, and microbial activity that lower CO2 emissions [4, 9]. Diversified cropping systems, such as intensive cropping,
crop rotation, and cover cropping, can increase SOC by increasing the quality and quantity of
crop residue returned to the soil compared with less diversified systems, such as crop-fallow,
monocropping, and no cover crop [4, 10]. Nitrogen fertilization typically stimulates N2O emissions [3, 11], but can have a variable effect on CO2 and CH4 emissions [10, 12]. Because N2O
emissions plays a major role in enhancing GWP and GHGI, practices that can reduce N fertilization rates without influencing crop yields can substantially reduce net GHG emissions [3, 4].
Management practices used for GHG mitigation sometime can have counter effects. For
example, no-till can increase N2O emissions compared with conventional till in humid regions
by increasing soil water content and denitrification, thereby offsetting the GHG mitigation
potential [13]. Incorporation of root residue can increase soil C sequestration, but root respiration and mineralization of crop residue and SOC can have negative impacts on GHG mitigation [10, 14]. Besides the direct effect of N fertilization on N2O emissions, indirect effects, such
as NH4 volatilization, N leaching, and urea hydrolysis in the soil can also counteract the mitigation potential [15]. All of these factors should be considered while calculating net GWP and
GHGI, regardless of management practices [3, 4, 16].
Several methods have been employed to calculate GWP and GHGI by using the SOC
method which considers ΔSOC as CO2 sink. Some have used the sum of CO2 equivalents of
N2O and CH4 emissions [17, 18, 19], while others [20, 21] have included CO2 equivalents of all
three GHGs. Still others have used CO2 equivalents of N2O and CH4 emissions and ΔSOC [22,
23, 24]. A full accounting of all sources and sinks of GHGs to calculate net GWP and GHGI
includes CO2 equivalents from farm operations, N fertilization, and other inputs in addition to
above parameters [3, 4, 8, 16, 25, 26, 27, 28, 29, 30]. Some have excluded N2O and CH4 emissions, but used CO2 equivalents of all other sources and sinks [7]. An alternative method
(hereby called the soil respiration method) of calculating GWP and GHGI includes substituting
ΔSOC by soil respiration and the amount of previous year’s crop residue returned to the soil [4,
27, 28, 29, 30, 31]. Each method has its own advantages and drawbacks which will be explored
in detail by comparing GWP and GHGI values below.
Information on the effects of soil and crop management practices on GHG emissions in
croplands is available [17, 32, 33, 34]. Relatively, little is known about the influence of management practices on GWP and GHGI. The objectives of this study were to: (1) conduct a meta-
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
2 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
analysis of global data available in the literature on the individual and combined effects of tillage,
cropping systems, and N fertilization rates on GWP and GHGI calculated by the SOC method in
cropland soils using partial or full accounting of all sources and sinks of GHGs, (2) relate GWP
and GHGI with the duration of the experiment and soil and climatic conditions, (3) compare
GWP and GHGI values calculated by the SOC and soil respiration methods, and (4) identify
improved management practices that can reduce net GWP and GHGI.
Materials and Methods
Data collection and management
Data on GWP and GHGI were pulled from the available literature from 57 experiments and 225
treatments from national and international regions and grouped by individual and combined management practices that included tillage, cropping systems, and N fertilization rates (Tables 1, 2, 3, 4
and 5) using all available resources (e.g. Web of Science, Goggle Scholar, SCOPUS, etc.). The
Table 1. Effect of tillage on net global warming potential (GWP) and greenhouse gas intensity (GHGI) calculated by using the soil organic C
method in various regions with different soil and climatic conditions.
Location
Soil type
Annual
precip.
Study
duration
Mean air
temp.
mm
yr
°C
Colorado, USA
Clay loam
382
5
10.6
Colorado, USA
Clay loam
382
3
10.6
Queensland,
Australia
Clay
728
4
17.2
Michigan, USA
Sandy
loam,
loam
890
1
9.7
Parana, Brazil
Clay
1400
1
23.0
Michigan, USA
Sandy
loam,
loam
890
9
9.7
Hyderabad,
India
Clay
1520
20
25.0
Colorado, USA
Clay loam
890
1
10.6
North Dakota,
USA
Sandy
loam
373
4
5.2
Tillage†
GWP‡
GHGI‡
kg CO2
eq. ha-1
yr-1
kg CO2 eq. Mg-1
grain or
biomass
NT
-15
18
CT
1479
143
NT
-516
-60
CT
1071
93
NT
403
158
CT
495
195
NT
2870
----
CT
11500
----
NT
-500
-32
CT
2900
172
NT
140
----
CT
1140
----
NT
8930
----
CT
10250
----
NT
-1253
----
CT
2264
----
NT
887
420
CT
1287
655
Parameters used to
calculate GWP/GHGI
Reference
N2O, CH4, ΔSOC, farm
operation, inputs
[26]
N2O, CH4, ΔSOC, farm
operation, inputs
[4]
N2O, CH4, ΔSOC
[23]
CO2, CH4, N2O
[21]
N2O, CH4, ΔSOC
[24]
N2O, CH4, ΔSOC, farm
operation, inputs
[16]
N2O, CH4, ΔSOC, farm
operation, inputs
[16]
N2O, CH4, ΔSOC, farm
operation, inputs,
irrigation
[8]
N2O, CH4, ΔSOC, farm
operation, inputs,
irrigation
[29]
†Tillage are CT = conventional till, NT = no-till.
‡Positive values indicate source and negative values sink of CO2.
doi:10.1371/journal.pone.0148527.t001
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 2. Effect of cropping systems on net global warming potential (GWP) and greenhouse gas intensity (GHGI) calculated by using the soil
organic C method in various regions with different soil and climatic conditions.
Location
Soil type
Annual
precip.
Mean
air
temp
Study
duration
mm
°C
yr
Cropping
system†
GWP‡
GHGI‡
kg CO2
eq. ha-1
yr-1
kg CO2 eq. Mg1
grain or
biomass
222
65
Colorado, USA
Clay
loam
382
10.6
5
C
C-S
508
60
Colorado, USA
Clay
loam
382
10.6
3
C
-557
-64
C-S
104
42
G
-3500
----
A
-20
----
PO
-105
----
W-W
578
----
W-L
396
----
W-Fx-W-P
779
----
W-Fx-W-W
953
----
C
690
48
C-S
1020
102
C-W-S
640
----
Af
-200
----
W-C-F
254
----
C
-498
----
G
-642
----
-1291
----
----
Michigan, USA
Saskatchewan, Canada
Nebraska, USA
Michigan, USA
Colorado, USA
Sandy
loam,
loam
Loam, silt
loam
Silty clay
loam
Sandy
loam
Clay
loam
890
250
600
890
382
9.7
5.2
11.0
9.7
10.6
1
3
1.5
1
1
Colorado, USA
Clay
loam
382
10.6
1
C
C-S
-553
Central North
Dakota, USA
Silt loam
407
6.0
1.5
W-F
1654
W-SF-RY
1660
Western North
Dakota, USA
Sandy
loam
373
5.2
4
B
971
B-P
771
250
Western
Montana, USA
Silt loam
453
6.2
2
Af
2187
310
W
5074
730
W-P/B-F
5191
1065
373
300
Parameters used to
calculate GWP/GHGI
Reference
number
N2O, CH4, ΔSOC,
farm operation, inputs
[26]
N2O, CH4, ΔSOC,
farm operation, inputs
[4]
CO2, CH4, N2O,
[21]
N2O, ΔSOC, farm
operation
[47]
N2O, CH4, ΔSOC,
farm operation, inputs,
irrigation
[25]
N2O, CH4, ΔSOC,
farm operation, inputs,
irrigation
[8]
N2O, CH4, ΔSOC,
farm operation, inputs,
[8]
N2O, CH4, ΔSOC,
farm operation, inputs,
irrigation
[8]
N2O, CH4, and ΔSOC
[52]
N2O, CH4, ΔSOC,
farm operation, inputs,
irrigation
[29]
N2O, CH4, ΔSOC,
farm operation, inputs
[28]
† Crops are A = alfalfa, B = barley, C = corn, F = fallow, Fx = flax, G = grass, P = pea, PO = poplar, L = lentil, S = soybean. Letters joined by hyphenation
indicates crop rotation, e.g. W-F = wheat-fallow rotation.
‡ Positive values indicate source and negative values sink of CO2.
doi:10.1371/journal.pone.0148527.t002
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 3. Effect of N fertilization rate on net global warming potential (GWP) and greenhouse gas intensity (GHGI) calculated by using the soil
organic C method in various regions with different soil and climatic conditions.
Location
Colorado, USA
Colorado, USA
Soil
type
Clay
loam
Clay
loam
Annual
precip.
Mean
air
temp.
Study
duration
mm
°C
yr
382
10.6
5
382
10.6
3
Crop
Corn, soybean
Corn, soybean
N
rate
GWP†
GHGI†
kg N
ha-1
kg CO2
eq. ha-1
yr-1
kg CO2 eq.
Mg-1 grain or
biomass
0
472
77
134
542
45
246
1197
102
0
-77
-19
134
449
-32
246
500
37
324
139
Queensland,
Australia
Clay
728
17.2
4
Wheat
0
90
575
214
California,
USA
Clay
368
15.0
2
Rice
0
3965
861
Colorado, USA
California,
Clay
loam
Clay
382
368
10.6
15.0
1
3
Corn, soybean
Rice
USA
California,
USA
Arkansas,
USA
North Dakota,
USA
Montana, USA
Clay
loam
Silt
loam
Sandy
loam
Loam
368
1200
373
350
15.0
12.5
5.2
6.2
3
3
4
4
Rice
Rice
Barley, pea
Barley, pea
80
4789
544
140
5437
463
200
5395
410
260
5507
445
0
-311
----
134
629
----
202
595
----
0
658
156
50
816
120
100
712
91
150
1491
188
200
1541
190
0
5061
844
50
6012
772
100
6768
687
0
1068
278
112
2018
265
168
2069
257
224
2238
286
0
926
617
101
1248
383
0
635
453
80
185
105
Parameters used
to calculate GWP/
GHGI
Reference
N2O, CH4, ΔSOC,
farm operation,
inputs
[26]
N2O, CH4, ΔSOC,
farm operation,
inputs
[4]
N2O, CH4, ΔSOC
[23]
N2O, CH4
[19]
N2O, CH4, ΔSOC,
farm operation,
inputs, irrigation
[8]
N2O, CH4
[18]
N2O, CH4
[18]
N2O, CH4
[18]
N2O, CH4, ΔSOC,
farm operation,
inputs, irrigation
[29]
N2O, CH4, ΔSOC,
farm operation,
inputs
[30]
(Continued)
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 3. (Continued)
Location
Beijing,China
Nanjing, China
Nanjing, China
Soil
type
Loam
Silt
loam
Silt clay
Annual
precip.
Mean
air
temp.
Study
duration
mm
°C
yr
600
10.0
6
1107
1107
15.4
15.4
2
2
Crop
Corn, wheat
Amaranth, Tug
choy, Bok choy,
Corriander
Rice, wheat
N
rate
GWP†
GHGI†
kg N
ha-1
kg CO2
eq. ha-1
yr-1
kg CO2 eq.
Mg-1 grain or
biomass
0
2702
369
247
2853
255
280
4309
350
0
-2347
-139
1475
-1650
5
1967
7700
109
0
5700
740
360
7210
500
432
6660
410
480
8360
580
Parameters used
to calculate GWP/
GHGI
Reference
N2O, CH4, ΔSOC,
farm operation,
inputs, irrigation
[54]
N2O, CH4, ΔSOC,
farm operation,
inputs
[55]
N2O, CH4, ΔSOC
[56]
† Positive values indicate source and negative values sink of CO2.
doi:10.1371/journal.pone.0148527.t003
PRISMA (Preferred Reporting Items for System Review and Meta-Analysis) guidelines (Fig 1) have
been followed for collection and meta-analysis of data. In each management group, GWP and
GHGI values were listed based on soil and climatic conditions, cropping systems, duration of study
in each location, and parameters used for calculations. In some studies where two or more treatments were arranged in split-plot arrangements to evaluate the effects of individual and combined
management practices on GWP and GHGI, values for main and split-plot treatments were used as
individual management practices and their interactions as combined management practices when
data are significantly different among treatments and interactions using General Linear Model
(GLM) and mean separation tests. For experiments with unbalanced treatments, main treatment
was considered as individual management practice when data were analyzed using the orthogonal
contrast test and other mixed treatments as combined management practices when analyzed using
GLM and mean separation tests, provided that differences among treatments are significant. When
main treatments cannot be separated in a combination of various treatments, such treatments were
considered as combined management practices and data were analyzed as above.
The GWP using the SOC method [3, 4, 29, 30] to compare the effect of management practices in data analysis included the following options for calculations:
Partial accounting data:
Partial accounting data : GWP
¼ CO2 equivalents from ðN2O þ CH4Þ emissions with or without CO2 equivalent from DSOC:ð1Þ
Full accounting data : GWP
¼ CO2 equivalents from ðN2O þ CH4Þ emissions þ CO2 equivalents from ðfarm operations
þ N fertilization þ other inputsÞ
CO2 equivalent from DSOC
ð2Þ
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 4. Effect of combined management practices (tillage, cropping system, and N fertilization) on net global warming potential (GWP) and greenhouse gas intensity (GHGI) calculated by using the soil organic C method in various regions with different soil and climatic conditions.
Location
Colorado, USA
Colorado, USA
Queensland,
Australia
Nebraska,
USA
Colorado, USA
Soil type
Clay loam
Clay loam
Clay
Silty clay
loam
Clay loam
Annual
precip.
Mean
air
temp.
Study
duration
mm
°C
yr
382
10.6
5
382
728
600
382
10.6
17.2
11.0
10.6
3
4
1.5
1
Combined
management
practices
GWP†
GHGI†
kg CO2
eq. ha-1
yr-1
kg CO2 eq.
Mg-1 grain or
biomass
CT-CC-N0‡
709
108
CT-CC-N1‡
1545
136
CT-CC-N2‡
2184
185
NT-CC-N0‡
234
46
NT-CC-N1‡
-459
-47
NT-CC-N2‡
210
19
NT-CB-N0‡
33
6
NT-CB-N2‡
983
113
CT-CC-N0‡
80
13
CT-CC-N1‡
1333
117
CT-CC-N2‡
1800
150
NT-CC-N0‡
-233
-50
NT-CC-N1‡
-436
-53
NT-CC-N2‡
-880
-77
NT-CB-N0‡
139
127
NT-CB-N2‡
68
-43
CT-SB-N0§
277
117
CT-SB-N90§
710
272
CT-SR-N0§
338
148
CT-SR-N90§
654
243
NT-SB-N0§
329
136
NT-SB-N90§
534
202
NT-SR-N0§
350
153
NT-SR-N90§
401
140
CC-F1¶
540
39
CC-F2¶
840
56
CB-F1¶
1020
104
CB-F2¶
1020
99
CT-CC-N0‡
1647
----
CT-CC-N1‡
2383
----
CT-CC-N2‡
2763
----
NT-CC-N0‡
-1766
----
NT-CC-N1‡
-1125
----
NT-CC-N2‡
-815
----
NT-CB-N0‡
-942
----
NT-CB-N2‡
-164
----
Parameters used
to calculate GWP/
GHGI
Reference
N2O, CH4, ΔSOC,
farm operation,
inputs
[26]
N2O, CH4, ΔSOC,
farm operation,
inputs
[4]
N2O, CH4, ΔSOC
[23]
N2O, CH4, ΔSOC,
farm operation,
inputs, irrigation
[25]
N2O, CH4, ΔSOC,
farm operation,
inputs, irrigation
[8]
(Continued)
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
7 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
Table 4. (Continued)
Location
Minnesota,
USA
North Dakota,
USA
Eastern
Montana, USA
Western
Montana, USA
Soil type
Loam, silty,
clay loam,
clay loam
Sandy
loam
Loam
Silt loam
Annual
precip.
Mean
air
temp.
Study
duration
mm
°C
yr
645
4.3
3
373
350
453
5.2
6.2
5.5
4
4
2
Combined
management
practices
BAU#
GWP†
GHGI†
kg CO2
eq. ha-1
yr-1
kg CO2 eq.
Mg-1 grain or
biomass
5000
1094
MAXC#
3500
978
OGGB#
4000
1183
IR-CT-B-NF††
1607
450
IR-CT-B-NO††
1099
730
IR-NT-BP-NF††
1045
290
IR-NT-B-NF††
1117
320
IR-NT-B-NO††
952
670
NIR-CT-B-NF††
1443
480
NIR-CT-B-NO††
998
660
NIR-NT-BP-NF††
496
210
NIR-NT-B-NF††
824
280
NIR-NT-B-NO††
656
410
CT-BF-N0‡‡
1153
836
CT-BF-N80‡‡
403
280
NT-BP-/N0‡‡
120
86
NT-BP-/N80‡‡
110
58
NT-B-N0‡‡
632
446
NT-B-N80‡‡
43
23
HA-A§§
927
150
HA-W§§
5500
730
HA-WP/BF§§
3638
650
SHG-A§§
3447
470
SHG-W§§
4647
430
SHG-WP/BF§§
7031
1480
Parameters used
to calculate GWP/
GHGI
Reference
N2O, CH4, ΔSOC,
farm operation,
inputs
[27]
N2O, CH4, ΔSOC,
farm operation,
inputs, irrigation
[29]
N2O, CH4, ΔSOC,
farm operation,
inputs
[30]
N2O, CH4, ΔSOC,
farm operation,
inputs
[28]
† Positive values indicate source and negative values sink of CO2.
‡ CT = conventional till, NT = no-till, CC = continuous corn, CB = corn-soybean rotation, N0 = 0 kg N ha-1, N1 = 134 kg N ha-1, and N2 = 56–246 kg N ha.
1
§ CT = conventional till, NT = no-till, SB = stubble burned, SR = stubble retained in the soil, N0 = 0 kg N ha-1, N90 = 90 kg N ha-1.
¶ CC = continuous corn, CB = corn-soybean, F1 = 130–140 kg N ha-1, 0 kg P ha-1, 0 kg K ha-1; F2 = 230–310 kg N ha-1, 45 kg P ha-1, 85 kg K ha-1.
# BAU = conventional till corn-soybean rotation with 143 kg N ha-1, 17 kg P ha-1, and 0 kg K ha-1; MAXC = strip till corn-soybean-wheat/alfalfa-alfalfa
rotation with 89 kg N ha-1, 32 kg P ha-1, and 28 kg K ha-1; OGCB = strip till corn-soybean-wheat/alfalfa-alfalfa rotation with 0 kg N ha-1, 0 kg P ha-1; and 0
kg K ha-1.
†† IR = irrigated, NIR = nonirrigated, CT = conventional till, NT = no-till, B = malt barley, BP = malt barley-pea rotation, NO = 0 kg K ha-1, and NF = 67–
134 kg K ha-1.
‡‡ CT = conventional till, NT = no-till, B = malt barley, BF = malt barley-fallow rotation, BP = malt barley-pea rotation, N0 = 0 kg K ha-1, N80 = 0 kg K ha-1.
§§ HA = herbicide application for weed control, SHG = sheep grazing for weed control, A = alfalfa, W = wheat, WP/BF = wheat-pea/barley mixture hatfallow rotation.
doi:10.1371/journal.pone.0148527.t004
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 5. Soil respiration method of calculating net global warming potential (GWP) and greenhouse gas intensity (GHGI) in various regions with
different soil and climatic conditions as affected by combined management practices (tillage, cropping system, and N fertilization).
Location
Colorado,
USA
Arkansas,
USA
Minnesota,
USA
North
Dakota, USA
Eastern
Montana,
USA
Soil type
Clay loam
Silt loam
Loam, silt,
clay loam,
clay loam
Sandy
loam
Loam
Annual
precip.
Mean
air
temp.
Study
duration
mm
°C
yr
382
10.6
3
1200
645
373
350
14.5
4.3
5.2
6.2
4
3
4
4
Combined
management
practices
GWP†
GHGI†
kg CO2
eq. ha-1
yr-1
kg CO2 eq.
Mg-1 grain or
biomass
CT-CC-N0‡
1953
133
CT-CC-N1‡
-1367
-45
CT-CC-N2‡
-1743
-162
NT-CC-N0‡
-833
-217
NT-CC-N1‡
-2990
-310
NT-CC-N2‡
-4300
-390
NT-CB-N0‡
9495
1340
NT-CN-N2‡
9850
865
NIR-C§
-1351
----
NIR-CT§
760
----
IR-CT§
951
----
NIR-SO
-455
----
IR-SO§
-965
----
NIR-S§
-301
----
IR-S§
-4
----
IR-R§
6632
----
NIR-W§
661
----
BAU¶
500
109
MAXC¶
9100
2542
OGGB¶
1220
361
IR-CT-B-NF#
-7793
-1950
IR-CT-B-NO#
-1495
-490
IR-NT-BP-NF#
-9169
-2490
IR-NT-B-NF#
-8112
-2150
IR-NT-B-NO#
-117
-10
NIR-CT-B-NF#
-7050
-2270
NIR-CT-B-NO#
-1752
-670
NIR-NT-BP-NF#
-6618
-2920
NIR-NT-B-NF#
-6243
-1920
NIR-NT-B-NO#
-1281
-510
CT-BF-N0††
114
83
CT-BF-N80††
-292
-203
NT-BP-/N0††
-1902
-1156
Parameters used to
calculate GWP/GHGI
Reference
Soil respiration, N2O,
CH4, crop residue, farm
operation, inputs
[4]
N2O, CH4, crop
residue, farm
operation, inputs,
irrigation
[31]
N2O, CH4, crop
residue, farm
operation, inputs
[27]
Soil respiration N2O,
CH4, crop residue, farm
operation, inputs,
irrigation
[29]
Soil respiration, N2O,
CH4, crop residue, farm
operation, inputs
[30]
(Continued)
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 5. (Continued)
Location
Western
Montana,
USA
Soil type
Silt loam
Annual
precip.
Mean
air
temp.
Study
duration
mm
°C
yr
453
5.5
2
Combined
management
practices
GWP†
GHGI†
kg CO2
eq. ha-1
yr-1
kg CO2 eq.
Mg-1 grain or
biomass
NT-BP-/N80††
-2107
-1109
NT-B-N0††
-574
-404
NT-B-N80††
-1944
-1002
HA-A‡‡
4970
780
HA-W‡‡
8740
1180
HA-WP/BF‡‡
5894
1040
SHG-A‡‡
7030
650
SHG-W‡‡
8574
1440
SHG-WP/BF‡‡
8868
1860
Parameters used to
calculate GWP/GHGI
Reference
Soil respiration, N2O,
CH4, crop residue, farm
operation, inputs
[28]
† Positive values indicate source and negative values sink of CO2.
‡ CT = conventional till, NT = no-till, CC = continuous corn, CB = corn-soybean rotation, N0 = 0 kg N ha-1, N1 = 134 kg N ha-1, and N2 = 56–246 kg N ha1
.
§ IR = irrigated, NIR = nonirrigated, C = corn, CT = cotton, SO = sorghum, S = soybean, R = rice, and W = wheat.
¶ BAU = conventional till corn-soybean rotation with 143 kg N ha-1, 17 kg P ha-1, and 0 kg K ha-1; MAXC = strip till corn-soybean-wheat/alfalfa-alfalfa
rotation with 89 kg N ha-1, 32 kg P ha-1, and 28 kg K ha-1; OGCB = strip till corn-soybean-wheat/alfalfa-alfalfa rotation with 0 kg N ha-1, 0 kg P ha-1; and 0
kg K ha-1.
# IR = irrigated, NIR = nonirrigated, CT = conventional till, NT = no-till, B = malt barley, BP = malt barley-pea rotation, NO = 0 kg K ha-1, and NF = 67–134
kg K ha-1.
†† CT = conventional till, NT = no-till, B = malt barley, BF = malt barley-fallow rotation, BP = malt barley-pea rotation, N0 = 0 kg K ha-1, N80 = 0 kg K ha-1.
‡‡ HA = herbicide application for weed control, SHG = sheep grazing for weed control, A = alfalfa, W = wheat, WP/BF = wheat-pea/barley mixture hatfallow rotation.
doi:10.1371/journal.pone.0148527.t005
All data: GWP = CO2 equivalents calculated both from partial and full accounting data. The
purpose of using all data option in the analysis was to compare them with partial and full
accounting data options and to examine if the relationships of GWP and GHGI with management practices can be improved when the all data option was used compared with using only
partial and full accounting data options.
In the soil respiration method [4, 29, 30, 31], GWP was calculated as:
GWP ¼ CO2 equivalents from ðCO2 ½excluding root respiration þ N2 O þ CH4 Þ emissions
þ CO2 equivalents from ðfarm operations þ N fertilization þ other inputsÞ
CO2 equivalent from previous year’s crop residue returned to the soil
ð3Þ
The GHGI in the SOC or the soil respiration method was calculated as:
GHGI ¼ GWP ðSOC or soil respiration methodÞ=grain or biomass yield:
ð4Þ
Although data from 60 experiments and 255 treatments were collected, only data from 57
experiments and 225 treatments were selected for meta-analysis which meets the specific criteria shown below:
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
10 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
Fig 1. The PRISMA (Preferred Reporting Items for System Review and Meta-Analysis) guidelines used collection and meta-analysis of data.
doi:10.1371/journal.pone.0148527.g001
1. All experiments should be conducted in croplands in the field. Croplands included both
uplands and lowlands under all agricultural irrigated and dryland crops. Number of crops
grown in a year should be two or less. Data on GWP and GHGI estimated by models were
excluded for analysis.
2. Treatments in the experiments should be replicated, randomized, and arranged in a proper
experimental design.
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
3. For GWP and GHGI calculated by using the SOC method, soil C sequestration should have
occur to a depth of 20 cm from the initiation of the experiment to the end of GHG measurement period. In the soil respiration method, experiments should have started in the previous
year where the amount of crop residue returned to the soil was known.
4. Measurement of GHG emissions should occur at regular events, including close measurements during episodic events, such as immediately following precipitation, irrigation, tillage, fertilization, and snow melts.
5. A time horizon of 100 yr should be used to calculate the CO2 equivalents of N2O and CH4
emissions which have 298 and 25 times, respectively, more global warming potential than
CO2 [35].
6. Carbon dioxide emissions associated with use of farm equipment for irrigation, tillage, fertilization, planting, herbicide and pesticide application, and harvest as well as manufacture
and application of fertilizers and other chemical inputs should be either calculated based on
the number of hours the equipment were used multiplied by CO2 emissions per liter of fuel
or estimated as shown by various researchers [7, 36, 37].
7. For comparing the effects of individual and combined management practices on GWP and
GHGI, the SOC method was used to calculate these parameters. Because of the limited availability of data, the soil respiration method was used only to evaluate the effect of combined
management practice on GWP and GHGI.
Statistical analysis of data
Meta-analysis of data was conducted by using procedure as suggested by various researchers
[38, 39, 40, 41]. Those data where excessive levels of GHGs were reported, such as due to high
rates of N fertilization, were discarded for analysis. A paired t-test was used for data analysis to
compare the effects of individual and combined treatments of improved vs. traditional management practices on GWP and GHGI by evaluating significant difference between practices
[42]. Individual improved management practices included no-till, crop rotation, increased
cropping intensity, perennial crops, and reduced N fertilization rates. Individual traditional
management practices included conventional till, monocropping, reduced cropping intensity,
annual crops, and recommended N fertilization rates. Combined improved or traditional management practices included combinations of two or more of these practices. Comparisons
included no-till vs. conventional till, crop rotation vs. monocropping, annual vs. perennial
crop, and combined improved vs. combined traditional management practice that included a
combination of these practices with or without cropping intensity and N fertilization rates. For
cropping intensity and N fertilization rates, regression analysis were conducted to determine
their relationships with GWP and GHGI [42]. For comparison of combined improved vs. combined traditional management practice, appropriate combination of individual treatments with
lower and higher GWP and GHGI, respectively, were selected.
Regression analysis was used to relate changes in GWP and GHGI due to improved vs. conventional management with duration of the experiment to examine if the changes vary with
time [42]. Multiple linear regressions were conducted to include soil and climatic factors (total
annual precipitation and mean air temperature) in these analyses to determine if the relationships can be improved. For analysis of the soil factor, soil texture was assigned to a numerical
value: coarse = 0, medium = 1, and fine = 2; where coarse refers to sand, loamy sand, and sandy
loam; medium refers to loam, silt, silt loam, sandy clay, and sandy clay loam; and fine refers to
clay, silty clay, clay loam, and silty clay loam [39].
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 6. Effect of various management practices on net global warming potential (GWP) and greenhouse gas intensity (GHGI) based on the metaanalysis. Values for difference between practices are denoted as mean (± standard error).
Management practice
GWP
GHGI
N†
Difference between practices
kg CO2 eq. ha-1 yr-1
N
Difference between practices
kg CO2 eq. Mg-1 grain or biomass
All data
9
1212–3598 = -2386* (±874)
6
292–1008 = -716 (±566)
Full accounting data
6
1362–2915 = -1553* (±430)
3
126–297 = -171* (±33)
Partial accounting data
3
924–4965 = -4041 (±2485)
3
458–1719 = -1261 (±1142)
Crop rotation vs. monocrop
11
987–674 = 313** (±84)
11
304–215 = 89 (±34)
Corn-soybean vs. continuous corn
4
270 –(-234) = 504* (±114)
4
68–16 = 52 (±52)
Small grain-legume vs. continuous small grain
3
649–834 = -185** (±8)
3
- - - -#
Cropping intensity (1.00 vs. 0.67)
11
827–1319 = -492 (±301)
6
225–572 = -347** (±64)
Cropping intensity (1.00 vs. 0.50)
11
827–853 = -26 (±426)
6
-----
Cropping intensity (0.67 vs. 0.50)
11
1319–853 = 466 (±21)
6
-----
Perennial vs. annual crop
11
(-604)– 885 = -1489*** (±278)
6
(-298)– 260 = -558** (±138)
Improved vs. traditional (SOC method)
9
297–2474 = -2177* (±671)
8
174–582 = -408* (±154)
Improved vs. traditional (Respiration method)
6
(-1909)– 5741 = -7650** (±1720)
5
-(620)– 1068 = -1688** (±315)
Tillage (NT vs. CT)‡
Cropping system§
Combined management practice¶
*Significant at P 0.05
**Significant at P 0.01
***Significant at P 0.001.
† Number of experiments included in the meta-analysis.
‡ Tillage is CT, conventional tillage; and NT, no-tillage. Full accounting data denotes calculation of GWP and GHGI by accounting all sources and sinks of
GHGs (N2O and CH4 emissions, farm operations, inputs, and soil C sequestration). Partial accounting data denotes partial accounting of sources and
sinks (N2O and CH4 emissions and/or soil C sequestration). All data denotes inclusions of both full and partial accounting.
§ Small grains include wheat and barley. Cropping intensity was calculated based on number of crops grown in a year.
¶ Combined management practices include combinations of tillage, cropping system, and N fertilization. Improved and traditional management practices
were treatments with lowest and highest GWP and GHGI that were calculated by the soil organic C (SOC) and soil respiration method, respectively.
# Insufficient data.
doi:10.1371/journal.pone.0148527.t006
Results and Discussion
Effect of tillage
A meta-analysis of nine experiments (Table 1) on the effect of tillage, when other practices,
such as cropping systems, fertilization, and farm activities were similar between tillage systems,
showed that no-till reduced GWP by 66% and GHGI by 71% compared with conventional till
when the all data option of the SOC method of calculating GWP and GHGI was used
(Table 6). Using the full accounting data option, no-till reduced GWP by 55% and GHGI by
58% compared with conventional till. With the partial accounting data option, the reductions
in GWP and GHGI due to no-till vs. conventional till were 81 and 73%, respectively. Differences in crop yields among cropping systems and variations in soil and climatic conditions
among regions resulted in different proportion of reductions in GWP and GHGI due to no-till
vs. conventional till. Variability in GWP and GHGI were high, with coefficient of variation
ranging from 19 to 91%.
It is not surprising to obtain high variability in GWP and GHGI due to management practices because of extreme variations in GHG values caused by large fluxes from episodic events
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
of tillage, fertilization, precipitation, and snow melt, while variations from farm operations, N
fertilization, and other inputs are low [3, 4, 8, 29, 30]. As a result, variability in GWP and
GHGI calculated by the partial accounting data option (62 to 91%) was higher than calculated
by the full (19 to 28%) accounting data option. The GHG emissions vary not only from one
region to another due to differences in soil and climatic conditions, but also by diurnally, seasonally, and annually due to changes in soil temperature and water content in the same region
[8, 16, 43]. Reductions in GWP and GHGI in no-till compared with conventional till, regardless of the option used for calculation, showed that increased C sequestration due to reduced
soil disturbance and C mineralization reduced GWP and GHGI in no-till [3, 16, 44]. Conventional till increases crop residue incorporation and microbial activity, thereby reducing C
sequestration, but increasing GWP and GHGI compared with no-till [8, 45].
Lower GWP and GHGI values due to no-till vs. conventional till in the partial than the full
accounting data shows that the partial accounting data calculated greater GHG sink due to the
effect of tillage than the full accounting data. This is because CO2 equivalents from farm operations, N fertilization, and other inputs were not accounted in the partial accounting data.
Because all other farming operations were similar, the difference between no-till and conventional till was the use of several tillage operations to prepare a seed bed in conventional till,
while soil was not disturbed in no-till. As a result, CO2 emissions associated with burning of
fossil fuel for tractor operation was higher in conventional till than in no-till. Also, ΔSOC was
lower in conventional till than no-till. The result was greater differences in GWP and GHGI
values between no-till and conventional till in the partial than the full accounting data. It was
not surprising that GWP and GHGI values due to no-till vs. conventional till calculated by the
all data option were between full and partial accounting data options. Therefore, CO2 emissions associated with farm operations, N fertilization, and other chemical inputs should be
taken into account in addition to those from GHG emissions and soil C sequestration while
calculating net GWP and GHGI from agroecosystems.
Changes in GWP and GHGI due to no-till vs. conventional till with experiment duration
were linear to curvilinear for all and full accounting data, but linear for partial accounting data
(Fig 2). Availability of limited data resulted in fewer data points for GWP and GHGI in the partial accounting data. Both GWP and GHGI increased from 0 to 12 yr of experiment duration
and then declined for all and full accounting data, but increased with increased duration of
experiment for partial accounting data. This could be explained by several factors: (1) no-till
can some time increases N2O emissions due to increased soil water content and denitrification
compared with conventional till, especially in the humid region, thereby increasing GWP and
GHGI [13, 16, 44], (2) the potential for soil C sequestration using no-till decreases and reaches
a steady state as the duration of the experiment increases [44, 46], and (3) there is a high uncertainty in spatial and temporal variability in GHG emissions within and among regions due to
variations in soil and climatic conditions and management practices [8, 16, 29, 30]. As C
sequestration rate decreases due to increased C saturation with increased duration of the experiment [40, 44], GWP and GHGI may increase. When soil and climatic conditions, such as soil
texture, annual precipitation, and average air temperature of the experimental sites were
included in the multiple linear regressions, the relationships were dramatically improved (as
indicated by higher R2 and lower P values) (Tables 7 and 8). While air temperature had a negative effect on GWP and GHGI, the effect of soil texture varied. As increased air temperature
can increase GHG emissions due to accelerated mineralization of SOC, it is likely that
increased temperature enhanced rate of SOC mineralization more than the rate of GHG emissions. As a result, temperature had a negative effect on GWP and GHGI. The potentials for
reducing GWP and GHGI using no-till compared with conventional till, however, exist after
12 year. This is similar to that found by Six et al. [43] who reported that the benefit of no-till in
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
14 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
Fig 2. Changes in net global warming potential (GWP) and greenhouse gas intensity (GHGI) due to no-till (NT) vs. conventional till (CT) with the
duration of the experiment using the soil organic C method. Full accounting data denote calculations of GWP and GHGI by accounting all sources and
sinks of CO2 (N2O and CH4 emissions, farm operations, inputs, and soil C sequestration) and Partial accounting data, partial accounting of sources and sinks
(N2O and CH4 emissions and/or soil C sequestration). All data denotes inclusions of full and partial accounting data.
doi:10.1371/journal.pone.0148527.g002
reducing GWP and GHGI compared with conventional till was achieved only after 10 yr. Nevertheless, more long-term experiments are needed to relate the effect of tillage with duration of
experiment on GWP and GHGI.
Effect of cropping system
An evaluation of eleven experiments on cropping system containing small and large grain
crops showed that crop rotation increased GWP by 46% and GHGI by 41% compared with
monocropping (Tables 2 and 6). This was especially true for large grain crops, such as corn
(Zea mays L.) and soybean (Glycine max L.), where GWP and GHGI were 215 and 325%,
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
15 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
respectively, greater under corn-soybean than continuous corn (Table 6). In contrast, for small
grain crops, such as barley (Hordeum vulgare L.) and pea (Pisum sativum L.), GWP was 22%
lower under barley-pea than continuous barley. As cropping intensity increased, GWP and
GHGI reduced (Table 6, Fig 3). Both GWP and GHGI were 168 and 215%, respectively, lower
with perennial than annual cropping systems. Lack of sufficient data on crop yields prevented
for comparison of some treatments for GHGI.
Increased ΔSOC due to greater crop residue returned to the soil reduced GWP and GHGI
under continuous corn than corn-soybean rotation, although N fertilization rate to produce
sustainable yield was higher in continuous corn [4, 8, 25, 26]. In contrast, greater N2O emissions following soybean increased GWP and GHGI in corn-soybean rotation [4, 8, 25, 26].
Under small grain crops, however, several researchers [29, 30, 43, 44, 47] have found that
including legumes, such as pea and lentil (Lens culinaris L.), in rotation with nonlegumes, such
as wheat (Triticum aestivum L.) and barley, reduced GWP and GHGI compared with continuous nonlegumes. They observed this because (1) no N fertilizer was applied to legumes compared with nonlegumes which required large amount of N fertilizers to sustain yields, as N
fertilizer stimulates N2O emissions and (2) legumes supplied greater amount of N to succeeding crops due to higher N concentration when above- and belowground residues were returned
to the soil and reduced N fertilization rate than nonlegumes. Sainju et al. [29, 30] also found
that legume-nonlegume rotation increased ΔSOC because of increased turnover rate of plant C
to soil C compared with continuous nonlegume. Greater number of experiments and magnitude of changes, however, resulted in higher GWP and GHGI with monocropping than crop
rotation under large than small grain crops when values were averaged across experiments during data analysis.
Greater crop residue returned to the soil and increased ΔSOC reduced GWP and GHGI
when cropping intensity was increased [28, 29]. Enhanced C sequestration with increased compared with reduced cropping intensity in the semiarid regions with limited precipitation has
been well known [48, 49]. Several researchers [8, 28, 29] have found that fallowing or crop-fallow rotation increased GHG emissions and therefore GWP and GHGI compared with continuous cropping due to increased soil temperature and water content that enhanced microbial
activity and absence of crops to utilize mineralized N during fallow. Perennial crops can reduce
GWP and GHGI due to higher root biomass production [50, 51] and increased C sequestration
[12, 45] compared with annual crops [8, 21, 28]. Perennial crops are not usually tilled or
applied with fertilizers, herbicides, and pesticides, which reduce GHG emissions compared
with annual crops [3].
Changes in GWP and GHGI due to crop rotation vs. monocrop, corn-soybean vs. continuous corn, and perennial vs. annual crop decreased with increased duration of experiment (Fig
3). This suggests that GWP and GHGI can be reduced in the long term by using improved
cropping systems, such as crop rotation, intensive cropping, and perennial crops compared
with monocropping, crop-fallow, and annual crops Although corn-soybean increased GWP
and GHGI compared with continuous corn in the short term (Table 6), increased C sequestration rate in the long-term may reduce GWP and GHGI with corn-soybean with increased
duration of the experiment. It may be possible that duration of obtaining C saturation may be
shorter in continuous corn due to higher C sequestration rate than corn-soybean [40]. The
relationships were further strengthened, especially for GWP, when soil and climatic conditions
were accounted in the multiple linear regressions of GWP and GHGI with the duration of the
experiment (Tables 7 and 8). Soil texture had a positive effect on GWP and GHGI for cropping
intensity, but negative effect on GWP for crop rotation vs. monocrop and perennial vs. annual
crop. The trend was opposite for mean air temperature while annual precipitation had small
effect. Because the magnitude of ΔSOC is lower and time for C saturation is longer for cropping
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
16 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
Table 7. Multiple linear regression analysis of net global warming potential (GWP) with management practices, duration of the experiment, total
annual precipitation, mean annual air temperature, and soil texture from various locations.
Management practice
Soil
texture
R2
Intercept
Cropping
intensity
N fertilization
rate
Duration of the
experiment
Total annual
precipitation
Mean annual air
temperature
All data
-680
-----
-----
302
-2
-339
3185
0.89
0.031
Full accounting data
-359
-----
-----
278
-2
-94
-336
0.97
0.012
Partial accounting data
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Model not full rank- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
P
Tillage (NT vs. CT)†
Cropping system‡
Cropping intensity
2619
-2924
------
343
13
-1143
2751
0.86
0.003
Crop rotation vs.
monocrop
281
------
------
-119
-2
144
-96
0.79
0.019
Corn-soybean vs. corn
1604
------
------
-113
-2
------
------
0.90
0.005
Small grain-legume vs.
small grain
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Model not full rank- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Perennial vs. annual
crop
5127
------
------
-4824
-1
328
-757
0.99
0.002
0.011
N fertilization rate (kg N ha-1)†
All data
-599
------
7.6
-188
0.06
175
528
0.75
Full accounting data
-2872
------
3.2
94
106
-107
-292
0.93
0.0001
Partial accounting data
8043
------
7.2
-3513
-5.7
932
-2765
0.84
0.0001
Combined management practice§
Improved vs.
traditional (SOC
method)
-7695
-----
-----
757
7
-68
381
0.85
0.043
Improved vs.
traditional (Soil
respiration method)
-4753
-----
-----
59
4
-52
-1122
0.82
0.049
† Tillage is CT, conventional tillage; and NT, no-tillage. Full accounting data denotes calculation of GWP and GHGI by accounting all sources and sinks of
CO2 (N2O and CH4 emissions, farm operations, inputs, and soil C sequestration). Partial accounting data denotes partial accounting of sources and sinks
(N2O and CH4 emissions and/or soil C sequestration). All data denotes inclusions of full and partial accounting data.
‡ Small grains include wheat and barley. Cropping intensity was calculated based on number of crops grown in a year.
§ Combined management practices include combinations of tillage, cropping system, and N fertilization. Improved and traditional management were
treatments with lowest and highest GWP and GHGI that were calculated by the soil organic C (SOC) or soil respiration method.
doi:10.1371/journal.pone.0148527.t007
system than for tillage [44, 46], reduced GWP and GHGI for increased cropping intensity, crop
rotation vs. monocrop, and perennial vs. annual crop with increased duration of experiment
was probably due to increased C sequestration. The results showed that coarse texture soil can
enhance GWP and GHGI compared with fine texture when cropping intensity was increased,
but reduce GWP when crop rotation instead of monocropping or perennial instead of annual
crop were used. The reverse was true in regions with higher than lower air temperature.
Effect of nitrogen fertilization
The GWP decreased from 0 to 88 kg N ha-1 and then increased with increased N fertilization
rate for full and partial accounting data as well as all data option (Table 3, Fig 4). Similarly,
GHGI decreased from 0 to 213 kg N ha-1 and then increased with increased N rate for full
and partial accounting and all data options. At lowest GWP and GHGI, N rates for the full
accounting data were 88 and 145 kg N ha-1 compared with 45 and 213 kg N ha-1, respectively,
for the partial accounting data.
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Table 8. Multiple linear regression analysis of net greenhouse gas intensity (GHGI) with management practices, duration of the experiment, total
annual precipitation, mean annual air temperature, and soil texture from various locations.
Management
practice
Intercept
Cropping
intensity
N fertilization
rate
Duration of the
experiment
All data
1259
------
------
-80
Full accounting data
1638
------
------
32
Partial accounting
data
9428
------
------
-126
-1015
------
37
Mean annual air
temperature
Soil
texture
R2
P
2
-17
-1147
0.80
0.045
-44
5
------
0.94
0.015
-6
------
------
0.74
0.075
1
-328
757
0.94
0.0002
Total annual
precipitation
Tillage (NT vs. CT)†
Cropping system‡
Cropping intensity
2385
Crop rotation vs.
monocrop
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Model not full rank - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Corn-soybean vs.
corn
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Model not full rank - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Small grain-legume
vs. small grain
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Model not full rank - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Perennial vs. annual
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Model not full rank - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
N fertilization rate (kg N ha-1)†
All data
713
------
-0.18
-94
0.0004
7.4
-91
0.77
0.063
Full accounting data
33.0
------
-0.23
40
0.81
-57
146
0.82
0.002
Partial accounting
data
1034
------
0.33
373
-0.60
104
-336
0.73
0.0002
Improved vs.
traditional (Regular
method)
-1335
------
------
27
15
625
-377
0.76
0.079
Improved vs.
traditional(Alternative
method)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Model not full rank - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Combined
management
practice§
† Tillage is CT, conventional tillage; and NT, no-tillage. Full accounting data denotes calculation of GWP and GHGI by accounting all sources and sinks of
CO2 (N2O and CH4 emissions, farm operations, inputs, and soil C sequestration). Partial accounting data denotes partial accounting of sources and sinks
(N2O and CH4 emissions and/or soil C sequestration). All data denotes inclusions of full and partial accounting data.
‡ Small grains include wheat and barley. Cropping intensity was calculated based on number of crops grown in a year.
§ Combined management practices include combinations of tillage, cropping system, and N fertilization. Improved and traditional management were
treatments with lowest and highest GWP and GHGI that were calculated by the soil organic C (SOC) or soil respiration method.
doi:10.1371/journal.pone.0148527.t008
Regardless of the data option used for calculating GWP and GHGI, results showed that
increasing N rates to a certain level actually decreased GWP and GHGI, a case similar to that
reported by various researchers [4, 18, 29, 30, 52, 53]. These N rates probably corresponded to
crop N demand when crops used most of the soil available N, leaving little residual N in the
soil that reduced N2O emissions and therefore GWP and GHGI. When N rates further
increased, GWP and GHGI also increased, suggesting that excessive application of N fertilizers
can induce net GHG emissions. Therefore, GWP and GHGI can be reduced if N fertilization
rate can be decreased without affecting crop yields. Several researchers [3, 4, 29, 30] have
reported that N rates to crops can be decreased to reduce GWP and GHGI without influencing
crop yields. One practice is to use legume-nonlegume crop rotation where legume can reduce
N rate to succeeding nonlegume by supplying more N compared with continuous nonlegume.
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Fig 3. Changes in net global warming potential (GWP) and greenhouse gas intensity (GHGI) due to various cropping systems with the duration of
the experiment using the soil organic C method. Because of the lack of sufficient data, only the all data option method of calculating GWP and GHGI were
used for meta-analysis.
doi:10.1371/journal.pone.0148527.g003
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
19 / 26
Management Practices Impact on Net Greenhouse Gas Emissions
Fig 4. Relationship between N fertilization rate and net global warming potential (GWP) and greenhouse gas intensity (GHGI) using the soil
organic C method. Full accounting data denote calculations of GWP and GHGI by accounting all sources and sinks of CO2 (N2O and CH4 emissions, farm
operations, inputs, and soil C sequestration) and Partial accounting data, partial accounting of sources and sinks (N2O and CH4 emissions and/or soil C
sequestration). All data denotes inclusions of full and partial accounting data.
doi:10.1371/journal.pone.0148527.g004
Sainju et al. [29] have reported that N rate to dryland malt barley can be reduced by half by
adopting malt barley-pea rotation compared while continuous malt barley while maintaining
malt barley yield and quality. At 100 kg N ha-1 rate, GWP and GHGI were lower with the full
accounting data (259 kg CO2 eq. ha-1 yr-1 and 119 kg CO2 eq. Ma-1 grain or biomass, respectively) than the partial accounting data (2948 kg CO2 eq. ha-1 yr-1 and 371 kg CO2 eq. Ma-1
grain or biomass, respectively). This is in contrast to the effect of tillage where GWP and GHGI
were higher with full than partial accounting data. This suggests that, as with tillage comparison, GWP and GHGI values calculated by using the partial accounting data were overestimated
and that CO2 equivalents associated with farm operations, N fertilization, and other chemical
inputs should be accounted in addition to those from GHG emissions and ΔSOC when calculating net GWP and GHGI [21, 23, 24].
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
The relationships between GWP, GHGI, and N rate were further improved when duration
of the experiment and soil and climatic factors were taken into account in the multiple linear
regression (Tables 7 and 8). Duration of experiment and annual precipitation had positive
effects, but air temperature and soil texture had negative effects on GWP when the full
accounting data was used. With the partial accounting data, only air temperature had positive
effect on GWP, but other factors had negative effects. For GHGI, the factors having negative
effects were air temperature using the full accounting data and soil texture using the partial
accounting data. Annual precipitation had minor effect on GWP and GHGI, a case similar to
that observed for the effect of tillage.
Effect of combined management practice
Using the all data option, the improved combined management practice that included no-till,
diversified cropping system (crop rotation, increased cropping intensity, and perennial crop),
and reduced N rate decreased GWP and GHGI by 70 to 88% compared with the traditional
combined practice that included conventional till, less diversified cropping system (monocropping, crop-fallow, and annual crop), and recommended N rate (Tables 4 and 6). When compared with individual management practices, these reduction values were greater, such as 66 to
71% reductions for GWP and GHGI obtained with no-till vs. conventional till or -46 to -41%
reductions with crop rotation vs. monocrop. Using the soil respiration method, reductions in
GWP and GHGI were even higher, representing 133 to 158% reductions with the improved
combined management practice compared with the traditional combined management practice (Tables 5 and 6).
These results clearly showed that the improved management practice can reduce GWP and
GHGI compared with the traditional management practice, regardless of the methods used for
calculating GWP and GHGI. Further reduction in the magnitudes of GWP and GHGI showed
that the combined management practices may be more effective in reducing net GHG emissions than the individual practices, a case similar to those reported by various researchers [4, 8,
26, 29, 30]. The results also suggest that the soil respiration method may show greater GHG
sink values for comparison of management practices than the SOC method [4, 28, 29, 30].
Using the SOC method, changes in GWP due to improved vs. traditional combined management practice increased from 0 to 3.5 yr of experiment duration and then decreased (Fig 4).
Changes in GHGI, however, increased with increased duration of the experiment. In contrast,
changes in GWP and GHGI using the soil respiration method were either not affected by or
declined with the duration of the experiment. The relationships were further improved by
including soil and climatic factors in the multiple linear regressions (Tables 7 and 8). As with
the effect of individual management practices, some of the possible reasons for increased GWP
and GHGI for improved vs. traditional combined management with increased duration of the
experiment using the SOC method are: (1) high spatial and temporal variations of GHG emissions due to differences in soil and climatic conditions and management practices, (2) reduced
potential for soil C sequestration with increasing duration of the experiment, (3) use of full or
partial accounting data option for calculating GWP and GHGI, and (4) uncertainty in the
methods of measuring GHG emissions, such as variations in type and size of static chambers,
placement of chamber in the plot (row vs. inter-row or including vs. excluding plants in the
chamber), time of GHG measurement during the day, and calculation of GHG fluxes (linear or
nonlinear emissions with time). Results, however, indicate that GWP and GHGI can be
reduced in the long term as duration of the experiment is increased, regardless of the method
used. As a result, more long-term experiments may be needed to properly evaluate the effect of
combined management practices on GWP and GHGI.
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
Fig 5. Changes in net global warming potential (GWP) and greenhouse gas intensity (GHGI) due to improved vs. traditional management practice
with the duration of the experiment using the soil organic C and soil respiration methods. Because of the lack of sufficient data, only the all data option
method of calculating GWP and GHGI were used for meta-analysis.
doi:10.1371/journal.pone.0148527.g005
Comparison of the methods of measurement
Both GWP and GHGI using SOC and soil respiration methods were lower with the improved
combined management practice than the traditional combined management practice
(Table 6). The GWP and GHGI measured with the soil respiration method were three to four
times lower than those with the SOC method. Furthermore, changes in GWP and GHGI due
to combined improved vs. combined traditional management practice were either not affected
or decreased with the duration of experiment with the soil respiration method, but increased
with the SOC method (Fig 5). This indicates that improved management practices may act
more towards GHG sink than the traditional management practices using the soil respiration
method compared with the SOC method. Similar results have been reported by various
researchers [4, 28, 29, 30] who observed that most treatments were sources (or positive values)
of GWP and GHGI when measured by using the SOC method, but sinks (or negative values)
when measured by the soil respiration method. It is difficult to examine with the limited
amount of data at present about which method provides efficient and accurate measurement of
GWP and GHGI because of limitations, such as high variability in GHG emissions, slow
changes in soil organic C levels, and differences in crop yields from year to year due to climatic
conditions. More studies, however, are needed to accurately evaluate the effectiveness of each
method on GWP and GHGI as affected by management practices.
Several researchers [4, 8, 29, 30] have reported that GWP and GHGI calculated by the soil
respiration method had more variability than those calculated by the SOC method due to
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
differences in CO2 emissions and crop residue production from year to year. This was against
the results obtained in this study where coefficient of variations were 22% for GWP and 19%
for GHGI calculated by the soil respiration method compared with 31 and 38%, respectively,
calculated by the SOC method for combined management practices (Table 6). The SOC
method is also subjected to high spatial variability due to interference from soil inorganic C,
besides variability in GHG emissions [4, 8, 29, 30].
There are several benefits and drawbacks of the each method. The soil respiration method
may provide quick results for GWP and GHGI compared with the SOC method, since only
two years of experimentation are required when the amount of crop residue returned to the
soil in the previous year is known. Information on C contributions from roots and rhozideposit
from previous crop can further reduce GWP and GHGI calculated by this method [29, 30].
Such factors, however, are not usually measured in most experiments. Loss of previous crop
residue due to the actions of wind and water or crop failure due to drought, especially in dryland cropping systems, can contribute significant errors in the calculation of GWP and GHGI
in this method. Other needed information is soil respiration where the value of root respiration
is excluded. Although the SOC method is a standard method and requires fewer parameters for
calculating GWP and GHGI, it may take long time to measure these parameters using this
method, because the process of C sequestration is slow and ΔSOC depends on soil and climatic
conditions. In some cases, the benefits of management practices, such as no-till compared with
conventional till, in reducing GWP and GHGI by the SOC method may not be realized after 10
yr [44]. While soil respiration and the amount of crop residue returned to the soil were the
driving factors for GWP and GHGI in the soil respiration method, N2O emissions were the
dominant factor in the SOC method. This study showed that GWP and GHGI calculated by
the soil respiration method have potentials to decrease with increased duration of the experiment, which are in contrast to those obtained by the SOC method (Fig 5).
The notion that the soil respiration method showed greater reductions in GWP and GHGI
than the SOC method may sometime provide false conclusions, especially during dry years
when crop yields can be lower and GHG emissions can be higher, resulting in net CO2 source
[29, 30]. During years with above-average precipitation, crop yields and the amount of crop
residue returned to the soil can be greater, resulting in lower GWP and GHGI as measured by
this method. In contrast, C sequestration is largely controlled by soil and climatic conditions
among regions in the SOC method, although C input from crop residue can influence ΔSOC.
Carbon sequestration rate can be higher in fine than in coarse-textured soil [29, 30]. Similarly
ΔSOC can be greater in cold than in warm regions or higher in irrigated than dryland cropping
systems. These factors add uncertainty in the measurements of GWP and GHGI in the SOC
method. Both methods, however, showed that improved management practices, such as no-till
continuous cropping with optimum N fertilization rate, can reduce GWP and GHGI compared
with traditional practices, such as conventional till with crop-fallow and recommended N fertilization rate, a case similar to that reported by various researchers [4, 29, 30].
Conclusions
Analysis of available global data revealed that improved management practices, such as no-till,
diversified cropping systems, and reduced N fertilization rate, either as individually or in combination, reduced GWP and GHGI compared with traditional management practices, such as
conventional till, less diversified cropping system, and recommended N rate. Changes in GWP
and GHGI due to tillage practices were greater than changes due to cropping systems and N
rates. Improved combined management practices further reduced GWP and GHGI compared
with improved individual management practices. Adopting improved management practices
PLOS ONE | DOI:10.1371/journal.pone.0148527 February 22, 2016
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Management Practices Impact on Net Greenhouse Gas Emissions
for a longer period can further reduce GWP and GHGI. The GWP and GHGI values can be
overestimated when indirect GHG emissions due to farm operations, N fertilization, and other
chemical inputs were not accounted for. Both soil respiration and SOC methods showed similar results of measuring GWP and GHGI as affected by management practices. Although the
soil respiration method may provide quick results for GWP and GHGI which can be higher for
improved management practices than measured by the SOC method, greater variability in
GHG measurements and crop yields from year to year suggest that more long-term studies are
needed to accurately measure the effect of management practices on GWP and GHGI using
both methods.
Acknowledgments
Mention of trade names or commercial products in this publication is solely for the purpose of
providing specific information and does not imply recommendation or endorsement by
USDA. The USDA is an equal opportunity employer.
Author Contributions
Conceived and designed the experiments: UMS. Performed the experiments: UMS. Analyzed
the data: UMS. Contributed reagents/materials/analysis tools: UMS. Wrote the paper: UMS.
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