Global Biogeochemical Cycles
Supporting Information for
Biogenic carbon fluxes from global agricultural production and consumption
Julie Wolf1, Tristram O. West1*, Yannick L. Le Page1, G. Page Kyle1, Xuesong Zhang1,
G. James Collatz2, Marc L. Imhoff1
1
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College
Park, MD 20740
2
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Contents of this file
Texts S1 - S3
Table S5. Poultry groupings based on reported carcass weight
Table S8. Regional per-animal egg productivity per year
Table S10. Global food intake by food group in 2011
Table S11. Combined carbon loss and waste from the food supply chain
Table S12. Evaluation of observed food supply and food intake data to estimate
regional food intake values
Table S13. Regional per capita intake of animal-based and crop-based foods
Figure S1. Comparison of reported crop harvested area with MODIS and
adjusted crop area
Figure S2. Net carbon exchange (NCE) of biogenic cropland carbon in year 2009,
in g C m-2 yr-1
Additional Supporting Information (Files uploaded separately)
Table S1. List of nations included in regional groupings of results
Table S2. Crop-specific coefficients to convert harvested biomass to carbon
Table S3. Coefficient uncertainty for major crops
Table S4. Coefficients to convert livestock feeds to carbon
Table S6. Regional groupings of nations for livestock calculations
Table S7. Annual feed intake and manure production per animal
Table S9. Coefficients to convert food supply quantities to carbon
1
Introduction
The auxiliary materials include three sections of text, 13 tables, and two figures
containing additional information on the derivation and values of coefficients, data
sources and processing, and evaluation of methods. Text S1 includes additional methods
for crop calculations, including derivation of crop-specific carbon contents of harvestable
biomass, uncertainty ranges of crop coefficients, treatment of moisture content of
reported hay and fodder crop harvests, and comparison of MODIS cropland area with
FAO reported crop harvested area per geopolitical region. Text S2 includes additional
methods for livestock, including derivation of livestock coefficients and uncertainty
ranges for livestock coefficients. Text S3 includes additional methods for food
calculations, including evaluation of national food intakes derived from reported food
supply quantities.
Text S1.
Derivation of crop-specific carbon contents of harvestable biomass
The carbon content of harvestable biomass (CCy) was estimated for groups of
similar crops, including fruits, non-fruit vegetables, grains, legumes, and oilseeds, as well
as for individual crops that did not fit into clear groups. For several crops within each
group, the contents (g 100 g-1) of protein, lipid, complex carbohydrates (e.g., cellulose,
starches), simple carbohydrates (i.e. sugars) and water in edible harvested materials were
from USDA [2013b]. Crop-specific dry matter carbon contents of harvested biomass
were calculated from each harvest item’s content of these components, using carbon
content values of 0.53 for proteins [Rouwenhorst et al., 1991]; 0.44 for complex
carbohydrates [Rouwenhorst et al., 1991]; and 0.40 and 0.75 for simple carbohydrates
2
and lipids, respectively, based on the molecular mass balances for sugars and vegetable
lipids [Stryer, 1988; Kamal-Eldin and Andersson, 1997]. Items with similar resulting
carbon contents fractions were grouped. For peanut and seed cotton, the proportion of
harvest weight made up by shell or lint was assumed to have carbon content of 0.44. The
resulting estimated carbon contents for groups of harvested materials were: 0.41 for all
fruits and for beets and potatoes; 0.44 for non-fruit vegetable crops and for cassava; 0.46
for grains and all legumes except soybean and peanut; 0.52 for soybean; 0.54 for seed
cotton, including seed plus lint; 0.60 for peanut, including shell; 0.62 for oilcrops and
oilseeds excluding soybean, peanut, coconut, and seed cotton; and 0.63 for coconut. The
values for crop-specific carbon content of harvested dry matter, harvest index, root to
shoot ratio, and dry-matter (DM) content of harvested material, with sources, are
provided in Table S2.
Comparison of MODIS cropland area with FAO reported harvested crop area
Dividing FAO harvested crop area by the MODIS cropland area in each
geopolitical region allows for a comparison of the two area estimates within 399
geopolitical regions worldwide. Results indicate that FAO and MODIS crop areas rarely
agreed within ±25% (Fig. S1). Uncertainty in and disagreement among global landcover
products is well documented [Fritz et al., 2011; Congalton et al., 2014]. Focusing on
cropland areas, Pittman et al. [2010] showed that MODIS cropland area best matched
inventory cropland area in regions of intensive corn or soybean cropping, but performed
more poorly in regions of wheat, rice, and/or non-intensive cropping. Our findings are in
agreement with these previous studies. Reconciling the MODIS data to match the FAO
land areas maintains the integrity of the inventory data while spatially distributing the
3
fluxes to the most likely geographic areas. Reconciling the MODIS data in this manner
was the approach used by West et al. [2014] and also used here to spatially distribute the
carbon fluxes.
Uncertainty ranges for crop coefficients
For ten crops producing the most NPP, ranges of values for harvest index, root to
shoot ratio, harvest material dry matter content, and harvest material dry matter carbon
content were compiled from the literature for use in Monte Carlo analysis of uncertainty
(Table S3). For remaining crops, coefficient ranges were approximated using relative
range around the mode of similar crops.
The range in the carbon content of cellulosic materials, used to calculate
uncertainty for non-harvested aboveground biomass, roots, and harvested stem and leaf
materials, was 0.40 – 0.50, based on carbon contents of stover from several crops [ElTayeb et al., 2012]. The relative range in cellulosic materials (mode − 9.1%, mode +
13.6%) was applied to all fruit and vegetable crops. The range in carbon content of
maize grain and soybeans were estimated as the mode ± 3.5% of the mode value, based
on reported variability in seed content of fat and protein reported by Reynolds et al.
[2005] and Bellaloui et al. [2010]. This uncertainty range was used for all seed harvests.
Moisture content of reported hay and fodder crop harvests
USDA reports “dry basis” hay and haylage production at 13% moisture [USDA,
2008; Russelle, 2013], whereas FAOSTAT does not indicate moisture content for its
reported “forage and silage” crop harvests. To reconcile this difference, total U.S. alfalfa
and grass harvest quantities reported for the years 2000 – 2011 by FAOSTAT were
compared with harvest quantities reported at 13% moisture by the USDA [2013a],
4
calculated as the sum of all U.S. state-level alfalfa and grasses hay and haylage
production. This comparison indicates that FAOSTAT reports “forage and silage” crop
harvests at 65% moisture (data not shown), which is an appropriate moisture level for
silage and haylage production [Undersander, 2013]. Based on this comparison we
consider all FAOSTAT “forage and silage” crop harvest quantities to have 65%
moisture. USDA does not report silage crop harvests at standard moisture [Hawthorn, C,
personal communication, Sept. 22, 2014]. USDA reports of maize and sorghum
harvested for silage were also assumed to have 65% moisture.
Text S2.
Derivation of livestock coefficients
Estimates of per-animal feed intake dry matter (Fdw) were obtained from IPCC
[1996] for dairy cattle, meat cattle, swine, buffalo, sheep, goats, horses, mules, and
camels. All Fdw values were considered to have the fractional carbon content and
uncertainty range of crop stover as described above (CCcell, 0.44). The Fdw values for
meat and dairy cattle are provided at the regional level. Sheep and swine Fdw values are
provided for developed and developing countries. A new category was implemented for
newly industrialized countries (NIC) to represent countries with intermediate
development status, where husbandry of these species is likely to be split between
traditional and modern methods. Brazil, China, India, Indonesia, Malaysia, Mexico,
Phillippines, South Africa, Thailand, and Turkey were classified as NICs based on their
economies [Mankiw, 2011]. Swine in NIC nations were assigned Fdw values calculated
as weighted 60% - 40% averages of the Fdw values for developed and developing
countries based on reported prevalence of industrial vs. low-intensity swine husbandry in
5
China [McOrist et al., 2011]. Sheep Fdw values in NICs were assigned an average value
of Fdw values for developed and developing countries. Meat and dairy buffalo Fdw values
are provided for animals in the Indian subcontinent and in all other parts of the world
[IPCC, 1996]. Values of Fdw given by IPCC [1996] for horses, goats, camels, and mules
and asses do not vary across nations or regions, and we did not refine them further.
Regional Fdw and Mdw values for meat and laying chickens, and global values for
turkeys, ducks, and geese and guinea fowl, were developed as follows. Values for Fdw
and Mdw were assembled for U.S. meat (Fdw(broilers)US and Mdw(broilers)US) and laying
chickens (Fdw(layers)US and Mdw(layers)US) from reported feed intake and manure production,
assuming that commercial poultry feed and manure are 0.85 and 0.25 dry weight,
respectively. For meat and laying chickens in other countries, and for the other poultry
species globally, feed intake and manure production were estimated based on reported
carcass weights. Mean carcass weights of meat chickens for the U.S. and all reporting
nations were assembled from FAOSTAT. Nations with similar meat chicken carcass
weights were aggregated into 11 poultry “regions” and the mean carcass weight (CW)
was calculated for each region (Table S5). These groupings were not based on
geographical proximity but on similar carcass weights. Global mean CW for turkeys,
ducks, and geese and guinea fowl were also assembled from FAOSTAT; all animals of
these species were considered to be raised for meat. Regional estimates of Fdw and Mdw
for meat chickens, and global estimates for the other fowl species, were constructed
using U.S. meat chicken Fdw and Mdw and the carcass weights (Eq. 16 and 17).
Fdw(broilers)i = Fdw(broilers)US × (CWi / CWUS)0.75
Eq. 16
Mdw(broilers)i = Mdw(broilers)US × (CWi / CWUS)0.75
Eq. 17
6
where i refers to the aggregate of nations in consideration for chickens or to the global
average for other poultry species, US indicates values for the U.S., and “broilers”
indicates values for animals raised for meat.
This calculation is based on the observed scaling of basal metabolic rate with
body mass0.75 of animals, both within and among species [White and Kearney, 2013].
Body weights of laying chickens were not readily available for comparison among
nations, so laying chicken Fdw and Mdw were developed for each of the 11 groups of
nations created for meat chickens using the appropriate meat chicken carcass weights
(Eq. 18 and 19).
Fdw(layers)i= Fdw(layers)US × (CWi / CWUS)0.75
Eq. 18
Mdw(layers)i= Mdw(layers)US × (CWi / CWUS)0.75
Eq. 19
where i refers to the aggregate of nations in consideration for chickens, US indicates
values for the U.S., and “layers” indicates values for laying hens. Resulting groupings of
nations for poultry are shown in Table S6.
Values of Mdw per animal for dairy cattle, meat cattle, and buffalo are provided by
IPCC [1996]. Values of Mdw for sheep, goats, and horses were estimated by multiplying
the IPCC Fdw of those species by the ratio of Mdw/Fdw for these livestock species in the
U.S. given by West et al. [2011]. The ratio of Mdw/Fdw for sheep in the U.S. was also
used to estimate Mdw from Fdw for camels, llamas, and alpacas, and the ratio for horses in
the U.S. was used to estimate Mdw from Fdw for mules and asses. Feed intake and manure
production for llamas and alpacas were estimated from values for camels, based on
weight ranges of 130-204 kg for llamas and 48-84 kg for alpacas, using body mass ratios
as described above. The carbon content of manure (CCm) was estimated using
7
documented manure nitrogen content and carbon:nitrogen ratios for different livestock
species [Cornell University Cooperative Extension, 1992]. Estimated CCm values were
0.50 for dairy cattle and buffalo; 0.46 for meat cattle and buffalo; 0.43 for swine; 0.48 for
poultry; 0.43 for sheep, goats, camels, llamas, and alpacas; and 0.48 for horses, asses, and
mules. Regional groupings of nations for these livestock types are available in Table S6,
and regional values for Fdw and Mdw are given in Table S7.
Emissions coefficients for MMCH4 and EFCH4 are from IPCC [1996]. Mean
annual temperature is needed to select regional temperature-dependent coefficients for
MMCH4 emissions from each livestock species; mean annual temperatures were obtained
at the national level for all countries and at the subnational level for large nations from
the Global Livestock Production and Health Atlas [FAO Animal Production and Health
Division]. Egg and milk production quantities are also needed to complete the livestock
carbon budget and to estimate ECO2. Regional, average per-animal milk production from
IPCC [1996] and per-animal egg production derived from FAOSTAT [2013] were
converted to units carbon using dry matter contents of 0.12 for milk and 0.24 for eggs,
and dry weight carbon contents of 0.52 for milk and 0.60 for eggs. Derived egg
production carbon quantities are shown in Table S8.
Uncertainty ranges for livestock coefficients
A literature review was undertaken to derive ranges for livestock intake and
emissions coefficients for use in Monte Carlo analysis. A number of factors can
introduce variability to feed intake for different livestock species, such as feed quality,
amount of work done, housed vs. free-ranging status, temperature, space allotment, and
other stressors. Intakes for all cattle and buffalo were given a range of the mode value
8
±43% of the mode value, based on the variability introduced by different feed types to
intake quality and quantity [Subcommittee on Feed Intake, Committee on Animal
Nutrition, National Research Council, 1987]. Intakes for all swine were given a range of
the mode value ± 25%, based on variability introduced by temperature [Subcommittee on
Feed Intake, Committee on Animal Nutrition, National Research Council, 1987]. The
intake range for meat chickens was the mode value ±7%, for laying chickens was mode ±
13%, and for other poultry species was the mode ± 11%, based on observed ranges of
flock intakes [Naber and Bermudez, 1990]. Feed intake for sheep was given an
asymmetric range of mode − 25% to mode + 10%, based on variability introduced by
feed quality [Subcommittee on Feed Intake, Committee on Animal Nutrition, National
Research Council, 1987], and intakes of goats, camels, horses, mules and asses, and other
camelids were also assigned this range. Ranges for manure production for all livestock
types were the same as for intake. Based on values from the literature, asymmetric
ranges for manure carbon content were mode − 33% to mode + 26% for meat cattle and
meat buffalo, mode − 38% to mode + 16% for dairy cattle and buffalo, mode − 38% to
mode + 3% for swine, mode − 40% to mode + 8% for sheep, mode − 25% to mode +
47% for all poultry, mode − 61% to mode + 5% for horses, mules, and asses, and mode −
57% to mode + 17% for goats, camels, and other camelids. Uncertainty ranges of mode
± 30% were used for enteric fermentation and manure management methane coefficients
[IPCC, 2006]. Variability in milk production was mode ± 25% based on reported
variability among milk production per cow reported for 38 nations [Hagemann et al.,
2012]. Variability in egg production per laying hen was mode ± 22% based on reported
variability in annual productivity of one- and two-year-old hens [Fukumoto, 2009].
9
Text S3.
Evaluation of national food intakes derived from food supplies
To estimate food intake, national food supplies reported by FAOSTAT were
adjusted to better reflect self-reported food intake in national surveys (see main text).
These adjustments were applied to broad regions, some of which encompass countries
with contrasting food supply characteristics (e.g., North Africa and sub-Saharan Africa
[World Health Organization, 2003]). In the U.S., self-reported food intake surveys have
known issues, particularly the underreporting of consumption. In the U.S. in recent
years, adult men and women were found to underreport total daily caloric intake by
respective averages of 10% and 18% of their expected total energy expenditures [Archer
et al., 2013]. The adjustments we made to reported food supply yielded estimated U.S.
food intake carbon quantities that were 115% - 120% of self-reported quantities (Table
S12); these may be good approximations of actual intake based on the findings of the
authors above. In contrast, the adjustments to food supply resulted in estimated intakes
that were slightly too low relative to reported intake in Bangladesh, the Czech Republic,
and the United Kingdom (Table S12). Because each food intake survey study was
conducted at the national level and employed unique methodologies [European Food
Safety Authority, 2011], we gave precedence to matching the well-characterized U.S.
trends in self-reported intake.
10
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14
Table S1. List of nations included in regional groupings for results
Table S2. Crop-specific coefficients to convert harvested biomass to carbon
Table S3. Coefficient uncertainty for major crops
Table S4. Coefficients to convert livestock feeds to carbon
Table S5. Poultry groupings based on reported carcass weight
Approx. carcass
Nations/regions
weight (kg)
U.S., Brazil, and Argentina
2.0
Mexico and Central America
1.7
Caribbean
1.3
Western Europe
1.4
Eastern Europe and Canada
1.6
Oceania
1.8
Southeast Asia
1.1
India
1.2
West Asia
1.3
China, East Asia and Central Asia
1.5
Africa
1.2
Turkeys (all nations)
9.3
Ducks (all nations)
1.9
Geese and guinea fowl (all nations)
3.5
Table S6. Regional groupings of nations for livestock calculations
Table S7. Annual feed intake and manure production per animal
Table S8. Regional annual egg production per animal
Egg production
Region
(kg C animal-1 yr-1)
Africa
0.73
Asia (all)
1.28
Oceania
1.51
Central and South America and Caribbean
1.56
Europe (all)
1.86
North America
2.23
Table S9. Coefficients to convert fresh-weight food supplies to carbon
15
Table S10. Global food intake by food group in 2011
Food group
Food Intake
Beer, wine, and other fermented beverages
8.1 ± 4.1
Cassava
32.2 ± 7.5
Cheese
5.0 ± 1.1
Distilled alcoholic beverages
4.2 ± 2.0
Eggs
6.1 ± 1.7
Fats (vegetable and animal)
57.8 ± 9.4
Fruits
14.5 ± 8.3
Grains
278.8 ± 30.0
Honey, molasses, other sweeteners
6.3 ± 1.6
Legumes and pulses
23.9 ± 4.0
Meat
32.9 ± 6.0
Milk
16.3 ± 3.6
Starchy roots excl. cassava
14.5 ± 2.8
Sugars
44.6 ± 10.8
Vegetables
22.2 ± 8.6
a
Units are Tg C yr-1 ± 1 standard deviation.
16
Table S11. Combined carbon loss and waste from the food supply chain*
2005
2006
2007
Africa
26.3
27.3
27.7
Ctr. America/Caribbean
5.4
5.6
5.7
E. Europe, W. Asia and Ctrl. Asia 28.5
28.8
28.9
N. America
22.3
22.3
22.4
Oceania
1.4
1.4
1.5
S., S.E., and E. Asia
117.5
118.9
121.3
S. America
11.4
11.6
11.8
W. Europe
25.4
25.5
25.7
2008
28.5
5.6
29
22.2
1.5
123.5
12.1
25.9
2009
29.1
5.6
29.2
22.2
1.6
124.9
12.2
25.9
2010
30
5.8
29.4
22.3
1.6
127.9
12.5
26
2011
31.2
5.9
29.7
22.4
1.6
130.2
12.7
25.9
Globe
238.2
241.3
245
248.4
250.6
255.5
259.7
-1
*Units are Tg C yr . Food intake and food supply chain losses together comprise total food supply as reported by FAOSTAT.
See main text for details.
0
Table S12. Evaluation of observed food supply and food intake data to estimate regional food intake valuesa
FAO
Survey
Estimated Estimated % of
Reported intake
Food
food
food
survey food
Nation
Year(s) Food survey source
quantities
supply Cb intake C intake C
intake C
1995Fat, protein, and
Bangladesh
1996
Hels et al., 2003
carbohydratesc
0.081
0.074
0.067
90%
EFSA: SISP04
Food commodity
Czech Republic 2004
Survey
categoriesd
0.135
0.089
0.076
86%
2003Food commodity
U.S.
2004
Bowman et al. 2013 categoriesd
0.154
0.075
0.087
115%
2007Food commodity
U.S.
2008
Bowman et al. 2013 categoriesd
0.150
0.070
0.084
120%
EFSA: NDNS
Food commodity
U.K.
2005
Survey
categoriesd
0.129
0.076
0.072
95%
a
-1
Excluding fish, seafood, and orchard products. Units are Mg C yr per capita except where otherwise indicated.
b
Calculated by converting all FAO reported food supplies to units carbon using our water and carbon content coefficients.
c
Converted to units carbon year using C contents of 75%, 53%, and 44% by weight for fats, proteins, and carbohydrates.
d
Food commodity groups (e.g. milk, cheese, fats, fruit, grains, meat) converted to carbon using our water and carbon content
coefficients.
1
Table S13. Regional per capita intake of animal-based and crop-based foods
2000
2001
2002
2003
2004
2005
2006
2007
2008
Per capita animal-based food intake (kg C yr-1)
Africa
5.1
5.0
5.3
5.5
5.5
5.5
5.7
5.8
5.8
Ctrl. America and Caribb.
12.6
12.9
13.3
13.2
13.6
13.7
13.7
14.2
14.0
E. Europe, W. and Ctrl.
Asia
14.0
13.9
14.4
14.6
14.6
14.9
15.1
15.5
15.4
N. America
23.6
23.9
23.8
23.8
24.4
23.9
23.8
23.9
23.1
Oceania
17.8
17.8
17.7
18.1
17.5
18.6
18.5
19.1
18.6
S., S.E., and E. Asia
6.1
6.1
6.2
6.4
6.6
6.7
7.0
7.2
7.4
S. America
16.6
16.3
16.3
16.2
16.4
16.3
16.9
17.4
18.2
W. Europe
24.7
24.8
25.0
24.7
24.3
24.2
24.2
24.4
24.1
Africa
Ctrl. America and Caribb.
E. Europe, W. and Ctrl.
Asia
N. America
Oceania
S., S.E., and E. Asia
S. America
W. Europe
89.5
73.1
89.9
74.0
89.0
74.3
Per capita crop-based food intake (kg C yr-1)
88.8
89.0
90.0
90.7
90.3
90.5
74.6
74.0
73.0
74.0
74.1
73.0
62.6
62.6
38.4
64.7
74.6
50.2
63.8
61.2
39.3
64.3
75.5
50.7
64.1
62.1
39.2
63.8
74.0
50.9
64.1
62.0
39.3
63.5
76.6
50.3
65.3
62.1
39.1
63.5
77.0
50.7
65.9
62.8
38.9
63.8
77.9
50.9
66.4
61.5
39.1
64.3
77.7
50.7
66.5
60.7
39.4
65.3
78.0
50.7
66.0
60.0
40.0
65.8
78.9
51.0
2009
2010
2011
5.7
14.2
5.9
14.0
6.0
13.8
15.6
22.7
18.0
7.5
18.2
23.8
15.7
22.6
18.1
7.8
18.7
23.9
15.9
22.3
19.4
7.8
19.2
23.7
90.7
72.6
91.2
73.5
92.3
74.0
65.9
59.5
40.2
65.9
78.3
51.2
66.3
59.5
40.0
67.0
78.8
50.9
66.5
59.7
39.6
67.2
79.1
50.6
2
Figure S1. Ratio of FAO cropland area to MODIS cropland area per geopolitical region.
3
Figure S2. Net carbon exchange (NCE) of biogenic cropland carbon in year 2009, in g C
m-2 yr-1.
4