Iizumi & Wagai (2019). Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration

Scientific

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Leveraging drought risk reduction

for sustainable food, soil and

climate via soil organic carbon

sequestration

toshichika iizumi

& Rota Wagai

Drought is a major risk in global agriculture. Building-up soil organic carbon (Soc) enhances soil

fertility and efficient use of rainwater, which can increase drought tolerance in food production. SOC
management demonstrates its benefit at various locations and is a promising means to achieve food
security and climate mitigation at once. However, no global assessment of its potential and co-benefits
gained from SOC enhancement has been presented. Here we evaluated the extent to which SOC
build-up could reduce agricultural drought risk. Using statistical analysis of spatially-explicit global
crop and soil datasets, we find that relatively small enhancement in topsoil (0–30 cm) organic carbon

content (oc

top

) could increase drought tolerance of the food production systems operating over 70% of

the global harvested area (particularly drylands). By closing the gap between current and upper limit
of tolerance levels through SOC addition of 4.87 GtC at the global scale, farmer’s economic output in
drought years would increase by ~16%. This level of SOC increase has co-benefit of reducing global
decadal mean temperature warming by 0.011 °C. Our findings highlight that progress towards multiple

development goals can be leveraged by Soc enhancement in carbon (c)-poor soils in drier regions

around the world.

Increasing the productivity and income of small farmers in developing world is a widely acknowledged means to

achieve food security

. More than 570 million family farmers cultivate nearly 80% of global agricultural land and

provide a dominant portion of food locally consumed

. Most of their lands are rainfed and susceptible to persis-

tent drought which is the most severe climatic disaster for global food production

. One drought event occurred

in the 1983–2009 period decreased agricultural gross domestic production by 0.8% on a global average basis,

with more severe damage in developing countries than in developed countries and in drier regions than in wetter

regions

. Droughts have also contributed to recent rise in hunger

. Drought risk reduction is therefore crucial to

ensure the progress towards the food security goal by 2030 (ref.

Building up SOC helps to maintain soil moisture at levels that enable crops to withstand water deficit

–

because (i) soil organic matter (SOM) in which SOC acts as a backbone has enormous water holding capacity

and (ii) SOM enhances the formation and stability of porous soil structure which ensures the adequate balance

of moisture and aeration for plants and soil biota

. Environmental co-benefit of SOM includes the increase in

biodiversity and nutrient storage as well as the reduction of soil erosion

. Various agricultural practices, such

as the incorporation of manure, compost, crop residues and biochar into soil, conservation tillage, cover crop,

mulching and agroforestry, have been implemented around the world to improve soil water holding capacity

and fertility

–

. Higher crop yields in normal years

and smaller yield losses in drought years

are well-known

benefits of these soil management.

Soil management also plays an important role for climate mitigation. Carbon sequestration potential of agri-

cultural SOC stock is well recognized

–

since global soils contain two to three times more C than the atmos-

phere

, and a substantial portion (37%) of the global ice-free land is in use for agriculture

. Recently, the dual

benefits of SOC enhancement for climate mitigation and food security are emphasized in the “4 per mil initia-

tive”

. Given the limitations of available C and nitrogen resources to achieve the annual increase of global soil C

by 4 per mil

–

, it is imperative to identify the agricultural areas most suitable for additional SOC build-up in

Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai,

Tsukuba, 305-8604, Japan. *email:

iizumit@affrc.go.jp

open

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terms of the economic and environmental benefits associated with SOC increase

. Here we show that higher SOC

content is beneficial for drought risk reduction in global food production. We examined two benefits of the SOC

enhancement: (i) agricultural economic output in drought year, which has implications for small farmer’s income

and access to food, and (ii) climate mitigation. We assessed four major crops (maize, rice, wheat and soybean),

which together accounts for two thirds of the world’s food calories.

Results

Geographical pattern of drought tolerance gaps.

Using the spatially-explicit global crop and soil data-

sets, we depict geographic pattern of drought tolerance gaps (DT

gap

) in landscape-level food production systems.

The gap in each 0.5° grid cell is derived by comparing estimated current drought tolerance level and its attainable

upper limit. For the crops, dry-year yields relative to normal yield (average yield in 1992–2008) are computed

(the left panels of Supplementary Fig. S1). Their area-weighted average is used to represent the current drought

tolerance level (the left lowest panel of Supplementary Fig. S1). The upper limit of drought tolerance is deter-

mined by identifying the areas of high dry-year yields within similar climate regime (the right lowest panel of

Supplementary Fig. S1 and Supplementary Fig. S2a).

Our analysis shows clear regional variation in the estimated DT

gap

. The areas with relatively large gap (food

production is susceptible to drought) appear over drylands, including the Mediterranean, south Africa, Australia,

western American Midwest and central Asia (Fig.

). In the areas with relatively high drought tolerance (DT

gap

is already small), the dry-year yield is only lower than the normal yield by ~10%. Such areas are distributed to

eastern Asia, western Europe and eastern North America.

Reduction of drought tolerance gaps in response to Soc increase.

The influence of SOC level on

gap

is nonlinear and C-poor soils in drier parts of the world show higher sensitivity (Fig.

). Small increase in

SOC from nearly zero to 4–9 kgC m

−

substantially reduces DT

gap

in drier climate zones (the ratio between mean

annual precipitation and potential evapotranspiration, P/PET

0.45). On the other hand, such effect diminishes

under wetter climate. In the drier zones, average DT

gap

sharply drops down to 16–28% point with SOC increase

(red lines in CCB-52 and CCB-83 of Fig.

). The steep decline in DT

gap

in the drier areas is observed until the

SOC content approaches 4–9 kgC m

−

. However, further SOC increase only slightly reduces DT

gap

. The mildly

wet climate zones (CCB-55 and CCB-86 of Fig.

; 0.45

≤

P/PET

1.0) show the same but less-prominent trend

compared to the drier zones. Furthermore, in C-rich soils (

10 kgC m

−

), the SOC effect becomes uncertain for

the mildly wet zones because data in C-rich areas is quite scarce. No reduction in DT

gap

is found in wetter climate

zones (CCB-89 and CCB-58 of Fig.

; 1.0

≤

P/PET). In short, the SOC effect on drought risk reduction is detected

over 70.3% of the global harvested area (29.1%, 14.9%, 13.3% and 13.0% for CCB-55, 52, 86 and 83, respectively)

with greater importance of moisture regime than temperature one.

Irrigation effect is hardly distinguishable at the global scale (Supplementary Fig. S3a), although the role of

irrigation in moderating drought impacts is known

. Rather it appears to increase average yield level in our

analysis (Supplementary Fig. S3b,c), that is consistent with ref.

which explains the causes of yield gaps. The irri-

gation dataset used here

reports irrigation-equipped areas but not the amount of irrigation water. It is often the

case that in drought years water is not available or not economically accessible for farmers. This in part explains

why the relationship between irrigation intensity and DT

gap

is scattered. Importantly, OC

top

is only weakly corre-

lated with irrigation intensity (Pearson’s correlation coefficient is –0.106 with p-value of

0.001 estimated using

two-tailed t-test; Supplementary Fig. S3d). Consequently, the risk that the SOC effect found here covaries with

irrigation effect is low.

Figure 1. Drought tolerance gaps (DT

gap

) for maize, rice, wheat and soybean production systems circa the year

2000. DT

gap

is indicated as the difference between dry-year average yield (% to normal yield) and its maxima

within the areas under similar climate regime. Gap in each grid cell is calculated as an area-weighted average

across the crops and is displayed when the harvested area of the crops is greater than 1% of the grid-cell extent.

Gray color indicates the areas with no yield data.

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Discussion

Our results have implications for climate mitigation and food security as well as soil fertility. Using the relation-

ships addressed here, we estimate that the amount of additional SOC stock required to close DT

gap

in global food

production systems is 4.87 GtC (17.53 GtCO

with the 90%-probability interval of 17.29–17.84 GtCO

). The

contributions from the mildly wet (CCB-55; 2.28 GtC) and drier zones (CCB-52; 1.94 GtC) are larger than those

from the wetter zones due to their high areal coverage (29.1% and 14.9% of the global harvested area for CCB-55

and CCB-52, respectively; Fig.

). Adequate SOC management may differ by climate zone and available labor,

machinery, equipment and infrastructure. In warm regions where microbial degradation of relatively labile C

(e.g., plant residue and manure) is rapid, application of chemically-recalcitrant C such as biochar is effective as

long as its production and distribution system is available

. Other options, including the labile C addition, are

more feasible in colder and drier environment where C degradation is slow. The corresponding CO

removal from

the atmosphere would avoid global decadal mean surface temperature warming of 0.011 °C, relative to preindus-

trial levels (1850–1900), with the uncertainty range of 0.008–0.014 °C mainly due to the variation among climate

models. The estimated climate mitigation is small in absolute terms but not negligible to limit the warming to

1.5 °C as global temperature has already increase by about 1 °C (ref.

Our study highlights that, for all the regions examined here, closing DT

gap

always increases economic outputs

from food production systems in drought years (Fig.

) and provides a guide to target the areas where SOC

Figure 2. The relationships between topsoil organic carbon content versus drought tolerance gap (DT

gap

) for

different climate zones (upper small panels) and schematic illustrating the “gap-closing” simulation conducted

in this study (lower large panel). Each panel indicates specific climate zone; upper and lower panel indicates

warmer and cooler zone, respectively; left, middle and right panel indicates drier, mildly wet and wetter zone,

respectively; and the climate zones are coded by centered climate bin (CCB; Supplementary Fig. S2a). The extent

of harvested areas that locate within the climate zones accounts for 95% of the global harvested area of the crops.

Red lines indicate the locally weighted scatterplot smoothing (LOWESS) curves derived using 100 bootstrap

replications to represent average relationships and their uncertainty; blue crosses indicate the SOC targets and

corresponding DT

gap

used in the “gap-closing” simulation; AH indicates the harvested area share by climate

zone; and n indicates the sample size.

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enhancement is prioritized. Higher agricultural economic output would increase the income of small farmers

and their access to food. In particular, relatively large increases in dry-year output are anticipated in Middle

East and North Africa (a 16% increase relative to what would occur with the current DT

gap

), Eastern Europe and

Central Asia (10%), Sub-Saharan Africa (9%) and Southeast Asia and Oceania (7%) (Fig.

and Supplementary

Fig. S4). On the other hand, the regions expected to have the largest increase in SOC stock is Southeast Asia and

Oceania (0.83 GtC or 26% of the current regional OC

top

stock over the harvested area of the crops) followed by

Latin America and Caribbean, North America and Eastern Europe and Central Asia (0.67 GtC each or 17%, 14%

and 22%, respectively) (Fig.

). Therefore, Middle East and North Africa and Southeast Asia and Oceania are the

regions where SOC enhancement is particularly recommended for drought risk reduction. Sub-Saharan Africa

and Eastern Europe and Central Asia follow these regions. Identification of the areas where SOC build-up has

co-benefits for multiple development goals is critical due to the persistent challenges from nutrient source limi-

tation, nitrogen fertilizer cost, and the competing demands for crop residues among SOC enhancement, feeding

and energy

. Our study reveals that SOC build-up targeting at C-poor soils in world’s drier regions is a lev-

erage to facilitate further progress towards multiple development goals in drought risk reduction, food security,

climate mitigation and soil fertility, simultaneously.

Building up SOC may be more costly in drier regions than in wetter regions due to limited productivity

and land cover, high susceptibility to soil erosion, low soil structural stability as well as high human pressure.

However, with appropriate institutional arrangements, dryland holds high potential for SOC buildup through

efficient water management by maximizing rainwater capture and soil water retention (e.g., rain harvesting, con-

servation tillage practices and selecting crops with high water use efficiency

A caveat is that our analysis is only relevant to the landscape scale. As indicated by the departures of many

grid-cell DT

gap

values from the average relationships (Fig.

), factors contributing to the variation in DT

gap

at a

local scale are lacking in this study. Because yield time series data are crucial to calculate dry-year average yields

and associated DT

gap

, the limited availability of high-resolution global, historical yield dataset hinders us con-

ducting a global analysis with the spatial resolutions at which local soil characteristics play a role in characterizing

gap

Methods

Food production system’s drought tolerance.

An indicator to represent the average tolerance of land-

scape-level food production systems to meteorological droughts circa the year 2000 was calculated using cli-

mate bin, growing-season precipitation and crop yields. The climate bins were defined by thermal and moisture

regimes and used for climate zoning at the global scale. Maize, rice, wheat and soybean were considered. The

calculation procedure consisting of three steps is described below.

Step1: meteorological droughts.

We addressed location-specific dry years during the 17 yr period cen-

tered on the year 2000 (1992–2008). Growing-season precipitation was computed for each crop, cropping season

of a crop and year using the global crop calendars

(the 0.5°-resolution and unfilled version) and global retro-

spective daily meteorological forcing dataset

(0.5°). In the crop calendars, maize, rice and wheat have two

cropping seasons (main/second for maize and rice and winter/spring for wheat), whereas soybean has one single

season. For the crops with two cropping seasons, the calculated growing-season precipitation for the individual

cropping seasons were averaged using the reported average production share by season in the 1990s (ref.

) as the

weights. Based on the calculated annual time series of growing-season precipitation of the crops, we selected the

two driest years. The selected samples roughly correspond to a 10% of the sample size (n

17) and represent years

with meteorological drought more severe than the threshold for a 1-in-8.5 yr recurrence.

Figure 3. Global increases in (a) drought-year average annual agricultural economic output from closing

drought tolerance gaps (DT

gap

) and (b) additional SOC required to close DT

gap

. Numbers in the right panel

indicate the additional SOC in percent relative to the current regional topsoil organic carbon stock over the

harvested area of the crops.

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Step 2: dry-year average yields.

Annual yields of the individual crops in the selected dry years were aver-

aged across the years and divided by the 17 yr average yield for the normalization (the left panels of Supplementary

Fig. S1). Their area-weighted average across the crops was used as an indicator representing average food pro-

duction level under meteorological drought conditions. Yield data of the crops for the period 1992–2008 were

obtained from the Global Dataset of Historical Yields version 1.2 (0.5°; refs.

–

; The details on the difference

across versions are available in Table 1 of ref.

Although the dry-year average yields used here represented meteorological droughts, the comparisons with

the estimates of drought-induced yield losses in rainfed condition

which represent agricultural droughts defined

by yield reduction showed that the lower dry-year average yields accompanied the more severe drought-induced

yield losses (Supplementary Fig. S6). The correlation values were low (0.134–0.293) in absolute terms but sta-

tistically significant (p

0.001) thanks to the large sample size. This indicates that the null hypothesis that the

correlation is zero was rejected; and that a positive linear relationship between the dry-year average yields and

the drought-induced yield loss estimates exists. Therefore, the dry-year average yields captured to some degree

the major characteristics of the estimated agricultural droughts. The drought-induced yield losses of ref.

mainly

considered rainfed conditions, whereas both rainfed and irrigated conditions were considered in this study. This

may in part explain their discrepancies. A comparison with other indicators of agricultural droughts would be

useful, but such dataset is not available in our knowledge.

Step 3: gap between current drought tolerance level and its upper limit.

Variations in the dry-year

average yield across locations within similar climatic regime must be attributed to differences in management,

technology and environmental factors other than climate (e.g., soil). An approach used in ref.

offers a good

analogue. For each climate bin, we selected the highest dry-year average yield value and deemed as the attaina-

ble upper limit of drought tolerance in a given climatic regime. We used climate bins shown in Supplementary

Fig. S17 of ref.

which were derived based on average annual growing degree days (GDD) with the base temper-

ature of 0 °C and average aridity index (that is, annual potential evapotranspiration of reference crop divided by

annual precipitation) in 1996–2005 (Supplementary Fig. S2a). By comparing the current drought tolerance level

(DT

(% to average yield); the left lowest panels of Supplementary Fig. S1) and its attainable upper limit (DT

(% to

average yield); the right lowest panels of Supplementary Fig. S1), we derived their gap (referred to as the drought

tolerance gap, DT

gap

−

(% point); Fig.

There was a concern that DT

gap

values were largely different between the individual crops and area-weighted

average due to effects of inter-crop differences in harvested area extent and yield sensitivity to precipitation

deficits. However, the comparisons showed that DT

gap

of the individual crops and their area-weighted average

resembled each other (Supplementary Fig. S5). Therefore, we adopted area-weighted average DT

gap

for the latter

analysis.

Soil organic carbon content.

The topsoil (0–30 cm) organic carbon contents (OC

top

) circa the year 2000

were obtained from the regridded Harmonize World Soil Database version 1.2 (ref.

). The original data at the

30-sec arc resolution was initially compiled in FAO

and then aggregated the data into the 0.05° resolution

We further aggregated the data into the 0.5° resolution. In the aggregation, we first spatially interpolated soil

map at the 0.05° resolution into the 5-min arc (0.083°) resolution using the inverse distance weighted averaging

method to have the common resolution with the harvested area map in 2000 (ref.

). The interpolated data were

aggregated to represent average level of OC

top

over the harvested areas of the crops located within a 0.5° grid cell

(Supplementary Fig. S7).

irrigation intensity.

The extent of irrigated and rainfed areas for the individual crops were obtained from

the Monthly Irrigated and Rainfed Crop Areas around the year 2000 dataset (MIRCA2000; ref.

). The data at the

5-min arc resolution were aggregated into the 0.5° resolution. Then the irrigation intensity (that is, the fraction of

irrigated area to harvested area) was computed crop by crop and their area-weighted average across the crops was

used for the analysis (Supplementary Fig. S8).

Average relationships between SOC and drought tolerance gap.

A nonlinear relationship between

top

and DT

gap

was addressed for each of the climate zones using a locally weighted scatterplot smoothing

(LOWESS) curve. The LOWESS method

is powerful in depicting an empirical relationship between two vari-

ables shown in a scatter plot. We also used the bootstrap resampling technique with replacement and generated

100 bootstrap replications. The LOWESS curve was estimated for each replication using the statistical package R

(lowess function with f

0.9; R version 3.5.0; ref.

) to account for the uncertainty in estimated LOWESS curves

associated with data. The estimated LOWESS curves were used as the representation of average relationship and

uncertainty when deriving anticipated DT

gap

level from the current OC

top

level. The average inflection point

across the LOWESS curves (blue crosses of Fig.

) was visually identified for each of the climate zones and used

for the “gap-closing” simulation described below.

Global SOC stock increase from closing drought tolerance gaps.

To estimate the amount of addi-

tional SOC required to close DT

gap

at the global scale, we set the SOC targets for each of the four climate zones

based on the relationships addressed earlier (blue crosses in CCB-52, 55, 83 and 86 of Fig.

). The average inflec-

tion point across the LOWESS curves was visually determined to be the SOC target (and the corresponding

gap

). Ths SOC targets indicate a climate-zone-specific hypothetical SOC level above which additional SOC

does not help reduce DT

gap

. The remaining climate zones (CCB-58 and 89) were not included in the estimation

and thus no SOC target was set because it is unlikely that SOC increases lead to closing DT

gap

in these areas. The

combinations of the SOC target and corresponding DT

gap

used here are shown in Supplementary Table S1.

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The main procedure of our “gap-closing” simulation is shown in the larger panel of Fig.

. We selected one

location (grid cell) that locates within either of the climate zones (CCB-52, 55, 83 or 86) and examined whether

gap

for that location is above the anticipated level according to the current SOC level (indicated by a red line

in the smaller panels of Fig.

). If not (e.g., B in Fig.

), the current drought tolerance level is already higher than

the anticipated level and therefore no simulation was conducted for that location because no further reduction in

drought risk is expected by SOC enhancement. If yes (e.g., A in Fig.

), we compared the two data points, A’ {the

current SOC level, the anticipated DT

gap

} versus the target point X {the SOC target, the corresponding DT

gap

and computed SOC increase and DT

gap

reduction between A’ and X (

SOC

and

gap

in Fig.

, respectively).

The average inflection point across the LOWESS curves described earlier was used as the target point. We com-

puted

SOC

and

gap

values 100 times using different bootstrap replications (Fig.

). LOWESS curves could

vary by data samples used. The 100 different curves were used to account for the uncertainty in LOWESS curve

associated with data. No simulation was conducted when the current SOC level is higher than the SOC target

(C in Fig.

) because it is uncertain whether SOC increases in C-rich areas (

9 kgC m

−

) lead to further DT

gap

reduction (Fig.

). These computations were repeated until all grid cells within the climate zones were completed.

contribution to average economic output in drought years.

The calculated

gap

(and

SOC

)

values mentioned above were used to estimate the increases in dry-year average agricultural economic output by

closing DT

gap

. For this calculation, grid-cell estimates of

gap

were first aggregated into a country scale. The

grid-cell dry-year average yield when DT

gap

is closed,

(t ha

−

), was computed:

−

(

)

/100

(1)

i j

gc, ,

gap,

where the subscript

and

indicates crop and grid cell, respectively;

indicates the dry-year average yield under

the current DT

gap

(% to normal yield);

gap

indicates the reduced DT

gap

along with SOC increase (% to normal

yield); and

indicates the normal yield (17 yr average yield; t ha

−

). We assumed that reduced DT

gap

(

gap

) is

common across the crops. Because normal yields are different in absolute terms across the crops, a common

gap

value gives different increases in dry-year average yield in absolute terms across the crops.

The country dry-year average annual economic output from food production systems when DT

gap

is closed,

(USD), was computed:

∑

(2)

i k

i j

gc,

gc, ,

where the subscript

indicates country;

indicates the harvested area in 2000 (ha) (ref.

);

indicates the num-

ber of grid cells within a country; and

indicates the country average producer price of a crop for the period

1998–2002 (USD t

−

). The country annual producer prices were obtained from FAO statistical database

. As

consumer prices are more strongly affected by demand-supply balance, oil prices and others than producer prices,

we used producer prices.

Additionally, the country dry-year average and normal-year economic outputs from food production systems,

both under the current drought tolerance gaps,

and

(USD), were calculated:

∑

(3)

i k

i j

g, ,

and

∑

(4)

i k

i j

For visualization purpose, these values were normalized using the normal-year economic output as the base

(

gc,

and

; Supplementary Fig. S4). We also calculated increases in country dry-year average annual

economic output from closing DT

gap

relative to what would occur with the current DT

gap

(

gc,

). The calcu-

lated country estimates of dry-year economic output increase (and SOC stock increase) from closing DT

gap

were

aggregated into nine regions using the classification of regions shown in Supplementary Fig. S9. The regional

aggregation of

gc,

and

was indicated by yellow bars and green bars of Fig.

, respectively. The

regional SOC stock increase computed based on

SOC

is shown in Fig.

contribution to climate mitigation.

The estimated amount of additional SOC stock over the global crop-

land topsoil in GtC (aggregated from

SOC

) was first converted into a value in GtCO

and further converted into

a global decadal mean surface temperature change relative to 1850–1900. A liner relationship between cumulative

total anthropogenic CO

emission from 1870 and global decadal mean surface temperature change relative to

1861–1880 is found in Figure SPM.10 of Intergovernmental Panel on Climate Change (IPCC) Working Group I

Fifth Assessment Report

. We recalculated this relationship using the bias-corrected daily mean 2-m air temper-

ature data of eight atmosphere-ocean coupled general circulation models (GCMs)

used in the Coupled Model

Intercomparison Project phase 5 (CMIP5)

(Supplementary Table S2). Cumulative CO

emission was calcu-

lated using the reported emission estimates from fossil-fuel burning, cement manufacture and gas flaring for the

period 1870–2009 (ref.

) and Representative Concentration Pathways (RCPs) for the period 2010–2100 (ref.

As a result, we obtained the warming rate of 6.376

−

°C (GtCO

)

−

in global decadal mean surface temper-

ature relative to 1850–1900 with the 90%-probability interval from 4.482

−

to 7.898

−

°C (GtCO

)

−

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associated with use of different GCMs (Supplementary Fig. S10). This modification was to shift the reference

time interval from 1861–1880 used in ref.

to 1850–1900 because the latter is utilized in the recent IPCC special

report

and enables us deriving a more robust estimate of global temperature in preindustrial levels. The same

approach is found in ref.

Data availability

The data that support the findings of this study are available in Supplementary Information.

Received: 15 May 2019; Accepted: 13 November 2019;

Published: xx xx xxxx

References

1. Frelat, R.

et al

. Drivers of household food availability in sub-Saharan Africa based on big data from small farms.

Proc. Natl Acad. Sci.

USA

113, 458–463 (2016).

2. Lowder, S. K., Skoet, J. & Raney, T. The number, size, and distribution of farms, smallholder farms, and family farms worldwide.

World Dev.

87, 16–29 (2016).

3. Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production.

Nature

529, 84–87 (2016).

4. Kim, W., Iizumi, T. & Nishimori, M. Global patterns of crop production losses associated with droughts from 1983 to 2009.

J. Appl.

Meteor. Climatol

https://doi.org/10.1175/JAMC-D-18-0174.1

(2019).

5. The State of Food Security and Nutrition in the World 2018.

http://www.fao.org/3/I9553EN/i9553en.pdf

(FAO, 2018).

Sustainable Development Goals. Goal

. Zero Hunger

https://www.un.org/sustainabledevelopment/hunger/

(UN, 2015).

Drought-resistant soils. Optimization of soil moisture for sustainable plant production

http://www.fao.org/docrep/009/a0072e/

a0072e00.htm

(FAO, 2005).

8. Mikanová, O., Šimon, T., Javůrek, M. & Vach, M. Relationships between winter wheat yields and soil carbon under various tillage

systems.

Plant Soil Environ.

58, 540–544 (2012).

9. Al-Kaisi, M. M., Douelle, A. & Kwaw-Mensah, D. Soil microaggregate and macroaggregate decay over time and soil carbon change

as influenced by different tillage systems.

J. Soil Water Conserv.

69, 574–580 (2014).

10. Benefits of farm level disaster risk reduction practices in agriculture. Preliminary findings.

http://www.fao.org/3/a-i7319e.pdf

(FAO,

2017).

11.

Soil Organic Carbon: the hidden potential

http://www.fao.org/3/a-i6937e.pdf%20

(FAO, 2017).

12. Freibauer, A., Rounsevell, M. D. A., Smith, P. & Verhagen, J. Carbon sequestration in the agricultural soils of Europe.

Geoderma

122,

1–23 (2004).

13. Pan, G., Smith, P. & Pan, W. The role of soil organic matter in maintaining the productivity and yield stability of cereals in China.

Agric. Ecosyst. Environ.

129, 344–348 (2009).

14. Feller, C., Blanchart, E., Bernoux, M., Lal, R. & Manlay, R. Soil fertility concepts over the past two centuries: the importance

attributed to soil organic matter in developed and developing countries.

Arch. Agron. Soil Sci.

58, S3–S21 (2012).

15. Oldfield, E. E., Bradford, M. A. & Wood, S. A. Global meta-analysis of the relationship between soil organic matter and crop yields.

Soil

5, 15–32 (2019).

16. Lal, R. Soil carbon sequestration impacts on global climate change and food security.

Science

304, 1623–1627 (2004).

17. Paustian, K.

et al

. Climate-smart soils.

Nature

532, 49–57 (2016).

18. Chabbi, A.

et al

. Aligning agriculture and climate policy.

Nat. Clim. Change

7, 307–309 (2017).

19. Lal, R., Negassa, W. & Lorenz, K. Carbon sequestration in soil.

Curr. Opin. Environ. Sustain.

15, 79–86 (2015).

20. Ciais, P.

et al

. In

Climate Change 2013: The Physical Science Basis

(eds Stocker, T. F.

et al

.) 465–570 (IPCC, 2013).

21. Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2.

Earth

Syst. Sci. Data

9, 927–953 (2017).

22. Minasny, B.

et al

. Soil carbon 4 per mille.

Geoderma

292, 59–86 (2017).

23. van Groenigen, J. W.

et al

. Sequestering Soil Organic Carbon: A Nitrogen Dilemma.

Environ. Sci. Technol.

51, 4738–4739 (2017).

24. Poulton, P., Johnston, J., Macdonald, A., White, R. & Powlson, D. Major limitations to achieving “4 per 1000” increases in soil

organic carbon stock in temperate regions: Evidence from long‐term experiments at Rothamsted Research, United Kingdom.

Glob.

Change Biol.

24, 2563–2584 (2018).

25. Amundson, R. & Biardeau, L. Opinion: Soil carbon sequestration is an elusive climate mitigation tool.

Proc. Natl. Acad. Sci. USA

115,

11652–11656 (2018).

26. Kucharik, C. J. & Ramankutty, N. Trends and variability in U.S. corn yields over the twentieth century.

Earth Interact.

9, 1–29 (2005).

27. Hawkins, E.

et al

. Increasing influence of heat stress on French maize yields from the 1960s to the 2030s.

Glob. Change Biol.

19,

937–947 (2013).

28. Mueller, N. D.

et al

. Closing yield gaps through nutrient and water management.

Nature

490, 254–257 (2012).

29. Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new

high‐resolution data set for agricultural and hydrological modeling.

Glob. Biogeochem. Cycles

24, GB1011,

https://doi.

org/10.1029/2008GB003435

(2010).

30. Woolf, D.

et al

. Sustainable biochar to mitigate global climate change.

Nat. Commun.

1, 56,

https://doi.org/10.1038/ncomms1053

(2010).

31. Weng, Z.

et al

. Biochar built soil carbon over a decade by stabilizing rhizodeposits.

Nat. Clim. Change

7, 371–376 (2017).

32. Summary for Policymakers. In

Global warming of 1.5

°C

(eds Masson-Delmotte V.

et al

.) 1–32 (IPCC, 2018).

33.

Carbon sequestration in dryland soils

http://www.fao.org/3/y5738e/y5738e00.htm

(FAO, 2004).

34. Plaza-Bonilla, D.

et al

. Carbon management in dryland agricultural systems.

A review. Agron. Sustain. Dev.

35, 1319–1334 (2015).

35. Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns.

Glob. Ecol. Biogeogr.

19,

607–620 (2010).

36. Iizumi, T.

et al

. Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in

projected temperature and precipitation extremes.

J. Geophys. Res. Atmos.

122, 7800–7819 (2017).

37. Iizumi T. S14 Global Meteorological Forcing Dataset.,

https://doi.org/10.20783/DIAS.523

(2017).

38.

Major World Crop Areas and Climatic Profiles

https://www.usda.gov/oce/weather/pubs/Other/MWCACP/MajorWorldCropAreas.

pdf

(USDA, 1994).

39. Iizumi, T.

et al

. Historical changes in global yields: major cereal and legume crops from 1982 to 2006.

Glob. Ecol. Biogeogr.

23,

346–357 (2014).

40. Iizumi, T.

et al

. Uncertainties of potentials and recent changes in global yields of major crops resulting from census- and satellite-

based yield datasets at multiple resolutions.

PLoS ONE

13, e0203809,

https://doi.org/10.1371/journal.pone.0203809

(2018).

41. Iizumi, T. Global Dataset of Historical Yields (GDHY) version 1.2.,

https://doi.org/10.20783/DIAS.528

(2018).

Page 8

Scientific

RepoRtS

(2019) 9:19744

https://doi.org/10.1038/s41598-019-55835-y

www.nature.com/scientificreports

www.nature.com/scientificreports/

42. Iizumi, T. Improvements in climate change risk assessment for global crop production systems, in

Climate Smart Agriculture for the

Small-Scale Farers in the

Asian

and Pacific Region

(eds. Shirato, Y. & Hasebe, A.) 191–200 (National Agriculture and Food Research

Organization (NARO) and Food and Fertilizer Technology Center (FFTC) for the Asian and Pacific Region, 2019).

43. Iizumi, T.

et al

. Responses of crop yield growth to global temperature and socioeconomic changes.

Sci. Rep.

7, 7800,

https://doi.

org/10.1038/s41598-017-08214-4

(2017).

44.

Harmonized World Soil Database (version 1.2)

http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML

(FAO, 2012).

45. Wieder, W. R., Boehnert, J. & Bonan, G. B. Evaluating soil biogeochemistry parameterizations in Earth system models with

observations.

Glob. Biogeochem. Cycles

28, 211–222 (2014).

46. Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded Harmonized World Soil Database v1.2.

https://doi.org/10.3334/

ORNLDAAC/1247

(2014).

47. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types,

and net primary production in the year 2000.

Glob. Biogeochem. Cycles

22, GB1022,

https://doi.org/10.1029/2007GB002947

(2008).

48. Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots.

J. Amer. Statist. Assoc.

74, 829–836 (1979).

49. R:

A language and environment for statistical computing

https://www.R-project.org/

(R Core Team, 2018).

50.

FAOSTAT

http://www.fao.org/faostat/en/#data

(FAO, 2018).

51.

Summary for Policymakers

. In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F.

et al

.) 3–29 (IPCC, 2013).

52. Iizumi, T. Bias-corrected CMIP5 GCM daily data.,

https://doi.org/10.20783/DIAS.524

(2017).

53. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An Overview of CMIP5 and the experiment design.

Bull. Amer. Meteor. Soc.

93, 485–498

(2012).

54. Boden, T. A., Marland, G. & Andres, R. J. Global, Regional, and National Fossil-Fuel CO

Emissions (CDIAC, 2016).

55. van Vuuren, D. P.

et al

. The representative concentration pathways: an overview.

Clim. Change

109, 5–31 (2011).

Acknowledgements

We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, which is

responsible for the CMIP, and we thank the climate modeling groups (listed in Supplementary Table S2) for

producing and making their model output available. For the CMIP, the US Department of Energy’s Program

for Climate Model Diagnosis and Intercomparison coordinated and led the development of the software

infrastructure in partnership with the Global Organization for Earth System Science Portals. T.I. was partly

supported by the Joint Research Program of Arid Land Research Center, Tottori University (30F2001) and

the Environment Research and Technology Development Fund (S-14) of the Environmental Restoration and

Conservation Agency of Japan. T.I. thanks Wonsik Kim for his comments on dry-year average yields and sharing

the drought-induced yield loss data.

Author contributions

T.I. designed the study, conducted the analysis, interpreted the results and wrote the manuscript. R.W. contributed

to the initial conceptualization, interpretation of the results, discussion, and the writing.

competing interests

The authors declare no competing interests.

Additional information

Supplementary information is available for this paper at

https://doi.org/10.1038/s41598-019-55835-y

Correspondence and requests for materials should be addressed to T.I.
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Iizumi & Wagai (2019). Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration

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