Deng et al. (2023). Building soil to reduce climate change impacts on global crop yield

Rosenzweig et al., 2014

;

Zhao et al., 2017

), especially in less-developed and warm areas such as

sub-Sahara Africa and Latin America (

Ortiz-Bobea et al., 2021

). Recent

modeling studies have also shown that, without effective adaptation,

warming may also reduce yields in cooler regions (

Asseng et al., 2015

;

Rosenzweig et al., 2014

).

Strengthening adaptations of agricultural systems is imperative to

reduce exposure and vulnerability to climate change (

IPCC, 2022

).

Global assessments have mainly focused on cultivar shifts and agro-

nomic management practices to enhance adaptation (

). A combination of cultivars (e.

g., higher heat tolerance) and management (e.g., irrigation) adaptations

could reduce yield losses due to warming by ~5% in the mid-21st

century (

Hasegawa et al., 2022

). However, the negative effects of

climate change on crop production cannot be fully offset by imple-

menting adaptation, especially in lower latitudes (

Hasegawa et al.,

2022

;

IPCC, 2022

). Innovative adaptation pathways are required to

address climate risks in agricultural systems, such as soil-based strate-

gies (

Hasegawa et al., 2022

IPCC, 2022

).

Resilient and productive soils are necessary to sustainably intensify

agriculture, to increase yields while minimizing environmental harm

(

Deng et al., 2020

Oldfield et al., 2019

). High-quality soils can buffer

climate variability in cropping systems and sustain yield stability (

). Recent experiment study indicates that

soil with higher clay content and lower sand content contributes to

higher maize biomass and yield (

Chukwudi et al., 2021

). Under climate

warming, maize yields in regions with high soil total nitrogen (TN)

generally increased in China (

Deng et al., 2020

). Soil organic carbon

(SOC), in particular, has been suggested as an integrated and repre-

sentative indicator of soil quality, which relates to soil biological and

physical properties such as disease suppressiveness, heat capacity (

Lal,

2013

;

Schlatter et al., 2017

) and soil heath, with important functions

such as water retention and nutrient supply (

). Improving SOC can help build climate resilience to

reduce risks to food insecurity (

Lehmann et al., 2020

;

Lipper et al.,

2014

), and decrease reliance on irrigation and fertilizer application

(

Oldfield et al., 2020

). A recent study has revealed that increasing SOC

can reduce the gaps between observed and attainable yields of maize

and wheat (

Oldfield et al., 2019

). However, the role of soil in building

crop yield resilience to climate change at the global scale is still largely

unknown in the crop-soil-environment system, it remains difficult to

quantify the complex interactions between soil, climate and yield

(

This study provides a soil-focused perspective to address escalating

climate challenges on global agriculture, and to look for opportunities in

soils for future food security. Here, we firstly determined the response of

maize, wheat, rice and soybean yields to warming temperature at grid

scale, i.e., temperature response index (TRI,%

◦

C

−

1

). Then, we identified

the role of soil properties (including SOC) in explaining spatially het-

erogeneous responses of crop yield to warming, by using a machine

learning approach. With outcomes from these processes, we finally

proposed soil related strategies for securing food production under

future climate change scenarios, and further explored the potential

impacts on food security.

2. Material and methods

2.1. Yield response to temperature

On the basis of historical data reflecting crop yields, climate and

management, the yield models (Eq.

(1)

) were developed for individual

crops (i.e., maize, wheat, rice, and soybean), and then used to identify

yield

’

s partial response to temperature (i.e., TRIs or temperature

response indices, Eq.

(3)

). Historical yields (1981

–

2010) of main crops,

maize (major), wheat (winter), rice (major) and soybean with the spatial

resolution of 0.5

◦

, were derived from GDHY v1.3, a global dataset of

historical yields of major crops with a data combination of agricultural

census, satellite and model (

Iizumi and Sakai, 2020

). The grids of a

certain crop with yield records of

<

20 years were excluded to avoid the

misallocation of planting area due to crop type changes. Historical daily

weather data were sourced from the AgMERRA, a post-processing

dataset of the NASA Modern-Era Retrospective Analysis for Research

and Applications (MERRA) for agricultural modeling (

Ruane et al.,

2015

). Average temperature (T), total precipitation (P) and solar radi-

ation (R) of crop growing season were extracted according to phenology

of each crop (

Sacks et al., 2010

). Both linear and quadratic forms of

temperature and precipitation were characterized in the model to ac-

count for the non-linear response of crop yields to climate (Eq.

(1)

). The

model has been widely applied in the studies of yield-climate relation-

ship (

Tigchelaar et al., 2018

), and

fully verified (

Liu et al., 2016

Lobell and Asseng, 2017

). Nitrogen (Nfer)

and phosphorus (Pfer) fertilizers (

Lu and Tian, 2017

) were further

included to better estimate the impacts of management on crop yields.

The input datasets with higher resolution were integrated to 0.5

◦

, to be

consistent with the resolution of yields (table S1). The model structure is

shown as:

ln

(

Y

i

,

t

)

=

β

0

,

i

+

β

1

,

i

t

+

β

2

,

i

T

i

,

t

+

β

3

,

i

T

2

i

,

t

+

β

4

,

i

P

i

,

t

+

β

5

,

i

P

2

i

,

t

+

β

6

,

i

R

i

,

t

+

β

7

,

i

Nfer

i

,

t

+

β

8

,

i

Pfer

i

,

t

+

ε

i

,

t

(1)

where ln(Y

i,t

) represents the logarithm of crop yields. Models of maize

(

n

=

14,134), wheat (

n

=

8406), rice (

n

=

9048) and soybean (

n

=

5996)

were developed for grid cell

i

(0.5

◦

×

0.5

◦

). The time term (

β

1,i

t

) was

used to simulate the possible impact of other factors on crop yields

(Fig. S1), e.g. cultivar shifts, which captured the trend of yield (

Zhu

et al., 2019

). As shown in this study (Fig. S2) and elsewhere (

), including the quadratic form (e.g., T

2

) can

better simulate the nonlinear responses of the crop to warming. The

response of crop yield to temperature was measured by the partial de-

rivative of Eq.

(1)

(

Zhu et al., 2019

):

∂

ln

(

Y

i

)

/

∂

T

i

=

∂

Y

i

/

(

Y

i

×

∂

T

i

) =

β

2

,

i

+

2β

3

,

i

T

i

(2)

where

∂

Y

i

/

Y

i

represents the proportion of yield change in grid cell

i

.

Temperature response index (TRI

i

) was defined as the partial yield

change (%) per

◦

C of temperature change, which can be measured as

X. Deng et al.

Science of the Total Environment 903 (2023) 166711

3

(

Wooldridge, 2018

):

TRI

i

≈

(

β

2

,

i

+

2β

3

,

i

T

i

)

×

100%

(3)

where

T

i

is the average temperature of the crop growing season during

1981

–

2010. The parameters

β

2,i

and

β

3,i

represent the location-specific

response of yield to temperature change. TRI varies spatially, with

values determined by grid level parameters and local climate. The TRI at

the continental and global scales was calculated on the basis of the area-

weighted average, considering geographic distribution of crop harvest

area derived from a gridded dataset (

Ray et al., 2012

). Modeling and

analysis were batched in Python version 3.6. More information on data

collection and use is also included in the Supplementary data.

2.2. Estimation of the role of soils

Soil plays a crucial role in providing nutrients, maintaining relatively

stable environment, and supporting crop growth as a whole (

Lal, 2020

;

Oldfield et al., 2020

). We hypothesized that the spatial heterogeneity of

TRI correlates to the differences of soil properties across space. The

machine learning approach, random forest, was employed to estimate

the correlation between TRI and soil properties due to its robustness

against overfitting and efficient modeling performance (

Breiman, 2001

).

WISE30sec dataset (

Batjes, 2015

) was selected for its comprehensive soil

properties and data sources. Six key soil properties (0

–

30 cm), SOC (%),

TN (%), cation exchange capacity (CEC, cmol kg

−

1

), clay content (%),

sand content (%), and pH were extracted by depth-weighted method and

resampled to 0.5

◦

resolution. Random forest models were built for in-

dividual crops, maize (

n

=

13,935), wheat (

n

=

8303), rice (

n

=

8925)

and soybean (

n

=

5935). Model training and testing were implemented

with the Scikit-learn Library in the Python. Three key parameters

needed to be adjusted for training the models, the number of trees in the

forest (

n_estimators

), the maximum depth of the tree (

max_depth

) and the

number of soil variables in the random subset at each node (

max_fea-

tures

), for the trade-off between over-fitting and high bias of the model.

When training, all decision trees in the forest were formed by the

method of bagging sampling with replacement to avoid reserving data

for testing or cross-validation (

Breiman, 2001

). In each tree, about one

third of samples were left out as out-of-bag data, which were then used

to estimate the generalization error (

).

The key soil properties were determined by the importance and the

interaction between TRI and each soil variable. The former was

measured by calculating the increase of prediction error after randomly

permuting the target soil variables in the random forest model. The

greater the increase in error, the more important the variable is. The

latter was visualized by centered individual conditional expectation (c-

ICE) plot (

Goldstein et al., 2015

). The curve in the plot showed how the

TRI changes when a soil variable changed after considering the average

effects of other variables. All individual samples were centered at a

certain point in the plot, which was helpful in examining the cumulative

effect of the selected feature. Besides, the c-ICE plot visualized the

condition of each individual sample (shaded areas on both sides of the

curve,

).

Through these analyses, SOC content was determined to be the most

important soil factor affecting crop response to temperature change,

followed by TN. From our global results, a linear relationship was

observed between SOC and TN (R

2

=

0.91), and this was further built

into the random forest model to estimate future TRI with SOC

improvement. In other words, improved yield resilience would be

realized by feeding in the potential of SOC improvement and associated

TN change. Specifically, the potential of SOC improvement was based on

the field-supported assumption that best management increases soil

carbon accumulation and then reaches up a relatively high and stable

SOC level (

Lessmann et al., 2021

). In this analysis, SOC data from

Zomer

et al. (2017)

was used for global-scale availability and accuracy. The

medium scenario was considered here with a sequestration rate of 0.56 t

C ha

−

1

yr

−

1

(0.9 Pg C yr

−

1

globally) lasting at least 20 years in the top of

30 cm (

Zomer et al., 2017

), by implementing practices including cover

cropping, manure application, and reduced tillage. The unit (%) and

resolution (0.5

◦

) of gridded SOC data were converted and integrated to

match the random forest model. The average TN content modeled

through the linear model was 0.19% (0.07

–

0.49%, 95% percentile).

2.3. Yield and production changes under future climate

With the changing temperature in the future, crop yield would

respond differently among crops following individual TRI pattern. The

highest and lowest additional radiative forcing scenarios (RCP2.6 and

RCP8.5), 2.6 and 8.5 W m

−

2

, respectively, were considered for future

climate scenarios (

). The monthly

temperatures of two scenarios were obtained from the outputs of global

climate models (GCMs) in CMIP6. According to the latest comparison of

the equilibrium climate sensitivity (ECS) (

Scafetta, 2022

), we chose

three GCMs with lowest ECS from different institutes, including INM-

CM5-0, CAMS-CSM1-0 and NorESM2-MM. To be spatially consistent

with other data, we aggregated the temperature data of above GCMs to a

0.5

◦

resolution. We averaged all model outputs for a relatively stable

and accurate temperature projection. According to the phenology data

of maize, wheat, rice and soybean, we extracted growing season tem-

perature between planting and harvest date. TRI under RCP2.6 and

RCP8.5 were estimated by using Eq.

(3)

based on the future warming in

2030, 2040 and 2050 relative to current level. The future crop yield

changes (Yc

i

,%) at grid cell

i

as a result of yield response (TRI

i

) and

future warming (RCP2.6 and RCP8.5) compared with the current tem-

perature (

Δ

T

i

):

Yc

i

=

TRI

i

×

Δ

T

i

(4)

Adopting SOC strategies would reduce the yield loss due to warming

(i.e., the difference of yield change (%) between scenarios with (Ya

i

) and

without (Yc

i

) SOC improvement). The avoided yield loss (%) at the

continental scale was obtained by area-weighting the results at the grid

scale (

Ray et al., 2012

), which was then multiplied by the current

average yield (Y) and harvest area (HA) (2011

−

2020) from FAO to es-

timate the avoided production loss (Pa).

Pa

=

∑

n

i

=

1

(

Yai

−

Yc

i

) ×

A

i

∑

n

i

=

1

A

i

×

Y

×

HA

(5)

The avoided production loss with SOC improvement (

Zomer et al.,

2017

) would lead to additional population that can be fed per year.

Following FAO approaches, the change of production was converted

into the calorie content (

FAO, 2012

), then divided by the calorie need

per capita per year to estimate the increased feed (

FAO, 2018

). The

results (i.e., yield loss, production loss, increased fed) were calculated

separately for four crops and two climate scenarios.

3. Results

3.1. Climate warming impacts on global crop yield

In general, warming has caused global-scale yield loss, with crop-

specific and spatially heterogeneous responses at finer scales (

Globally, the estimates for all four crops show negative TRIs, suggesting

an average yield loss of

−

3.4%

◦

C

−

1

(

−

32.0% to 19.1%

◦

C

−

1

, 95% dis-

tribution interval),

−

2.4%

◦

C

−

1

(

−

21.2% to 15.0%

◦

C

−

1

),

−

0.3%

◦

C

−

1

(

−

22.8% to 17.4%

◦

C

−

1

), and

−

5.0%

◦

C

−

1

(

−

25.2% to 14.3%

◦

C

−

1

) for

maize, wheat, rice, and soybean, respectively. However, crop- and

location-specific TRIs vary significantly. In particular, the TRIs for maize

are consistently negative across five continents, ranking from high to

low: Africa (

−

6.6%

◦

C

−

1

, ranging

−

39.2% to 26.1%

◦

C

−

1

), South

X. Deng et al.

Science of the Total Environment 903 (2023) 166711

4

America (

−

6.3%

◦

C

−

1

,

−

28.3% to 10.9%

◦

C

−

1

), North America

(

−

3.4%

◦

C

−

1

,

−

19.3% to 10.0%

◦

C

−

1

), Oceania (

−

2.8%

◦

C

−

1

,

−

42.2%

to 14.1%

◦

C

−

1

) and Europe (

−

2.2%

◦

C

−

1

,

−

15.7% to 10.9%

◦

C

−

1

). While

on average, maize in Asia is less negatively affected by warming, yield

loss is still observed in regions including Southeast Asia, Central Asia

and Northwest China (

a). For wheat, Africa is the most vulnerable

continent, with a TRI of

−

15.5%

◦

C

−

1

(

−

47.0% to 17.5%

◦

C

−

1

), followed

by South America (

−

7.3%

◦

C

−

1

,

−

22.3% to 11.8%

◦

C

−

1

) and Asia

(

−

1.1%

◦

C

−

1

,

−

20.7% to 18.7%

◦

C

−

1

) (

b). Rice is least affected by

rising temperatures in many regions, with continental scale TRIs closing

to zero (

c). The highest yield loss for soybean occurred in South

America (

−

9.8%

◦

C

−

1

,

−

25.5% to 12.9%

◦

C

−

1

), the largest soybean

producer, followed by Africa (

−

8.7%

◦

C

−

1

,

−

36.4% to 16.1%

◦

C

−

1

). The

lowest yield loss (

−

1.4%

◦

C

−

1

,

−

10.0% to 5.7%

◦

C

−

1

) appeared in North

America (

d). In general, crop production in Africa and South

America is more susceptible to warming.

3.2. The role of soil in reducing climate impacts on yield

The spatially heterogeneous response of crop yield to warming can

be largely explained by soil heterogeneity in terms of soil properties,

including SOC, TN, clay and sand content, pH and cation exchange ca-

pacity (CEC). After training and testing, the random forest model can

replicate the crop-specific yields to soil with the coefficient of determi-

nation (R

2

) of 0.46 to 0.66 (Fig. S3), and the relationships can be visu-

alized by centered individual conditional expectation (c-ICE) plot

(

). c-ICE plot can highlight the average change (colored curves)

and variation range (corresponding shadows) of TRI along with soil

properties (

a to

2

f), and also identify where, and to what extent,

heterogeneities might exist (

Goldstein et al., 2015

). Among six soil

properties that potentially affect crop growth, SOC is identified as the

most important predictor to TRI, followed by TN, considering the vari-

able importance metric (

g). Other soil properties do not affect TRI

consistently across the whole range (

c to

2

g). This implies that,

with increased SOC or TN (except maize) in locations where their cur-

rent levels are relatively low, TRI could be improved, suggesting

increased yield resilience to warming (e.g.,

a and

2

b). In particular,

with increasing SOC, the TRI of four crops would increase until reaching

a

“

plateau

”

(

a). When the SOC is lower than about 2.0%, increasing

SOC can considerably increase TRI, indicating improved yield resilience

to warming. Considering current low levels of SOC (Fig. S4), global soils

have great potential to increase carbon content before reaching the TRI

“

plateau

”

level. Current soil TN content, however, has already reached

the

“

plateau

”

level in most of the planting regions (Fig. S5), leaving

limited room for improving TRI via TN change. Therefore, in this study,

we further quantify spatial TRIs after soil improvement, specifically SOC

increase, with associated TN change to maintain soil C:N.

Our analysis shows that improving soil can generally lead to less

negative or more positive TRIs (

), relative to those with existing

soil conditions (

). By considering a

“

medium

”

sequestration sce-

nario that SOC increase rate in the top of 30 cm would achieve 26% of

the

“

4p1000

”

target (

Sommer and Bossio, 2014

Zomer et al., 2017

), the

SOC level after 20 years of sequestration can be increased by an average

of 1.3% in the study areas (Fig. S4). With the increase of SOC, the

warming-induced yield losses could be significantly reduced (

From a global perspective, the TRIs for maize, wheat, rice and soybean

would be 0.1%

◦

C

−

1

(

−

10.4% to 18.8%

◦

C

−

1

), 2.7%

◦

C

−

1

(

−

4.5% to

15.0%

◦

C

−

1

), 3.4%

◦

C

−

1

(

−

6.7% to 17.4%

◦

C

−

1

) and

−

0.6%

◦

C

−

1

(

−

11.2% to 14.2%

◦

C

−

1

), respectively. With improved yield resilience

owing to soil improvement, about 3.6%

–

5.1% of yield loss caused by a

degree Celsius warming can be avoided relative to the scenarios without

soil improvement.

Fig. 1.

Global temperature response indices (TRIs,%

◦

C

−

1

) of four crops. (

a

) Maize, (

b

) wheat, (

c

) rice and (

d

) soybean. TRI values show yield changes per

◦

C of

temperature increase, with positive and negative values indicating yield gain and loss, respectively. The black marks in the grids represent the significant influence of

warming. The box chart reflects the interquartile range and the middle line in the box represents the median. The boxes from left to right represent Africa (AF), South

America (SA), North America (NA), Oceania (OA), Asia (AS) and Europe (EU), and the blank indicates insufficient data in Oceania (

b

to

d

).

X. Deng et al.

$Figures and page graphics, page 5$

Science of the Total Environment 903 (2023) 166711

5

For maize, in the United States, the largest maize producer, the

average TRI would change from

−

3.7%

◦

C

−

1

to

−

1.5%

◦

C

−

1

, about 60%

of warming-induced yield loss could be avoided (

a). In West Africa

and East Africa, where yield has reduced by

>

30%

◦

C

−

1

in some areas

(

a), most of the loss would decrease to less than

−

10%

◦

C

−

1

after

improving SOC (

a). As for wheat, in both China and India, two of

the largest producers, the yield has suffered from different degree of loss

due to warming, about

−

0.1%

◦

C

−

1

and

−

7.0%

◦

C

−

1

, respectively. With

improved soil, the TRIs would turn positive in both countries (3.9%

◦

C

−

1

in China, and 1.1%

◦

C

−

1

in India), suggesting a potential yield benefit

with warming regardless of possible effects from other factors. It is not

unexpected that rice is less affected by warming as an irrigated crop, it

also benefits from SOC improvement. In particular, for China and India,

the top two rice producers, the average TRIs would increase from

1.0%

◦

C

−

1

and 0.3%

◦

C

−

1

to 3.5%

◦

C

−

1

and 4.7%

◦

C

−

1

, respectively,

showing even stronger yield resilience to warming. For soybean, its high

vulnerability to warming would also be significantly reduced, especially

in the main producing countries, Brazil, Argentina and India (

d).

The SOC strategy would reduce soybean yield loss by 8.2%

◦

C

−

1

in South

America (

d).

Globally, 53.2%, 67.8%, 51.8% and 71.6% of planting area for

maize, wheat, rice and soybean, respectively, could be given an

improved crop resilience due to increased SOC, covering 60.0% of global

total planting area (

,

and Fig. S6). Among these areas, 77.1%,

95.9%, 90.2% and 88.3% of maize, wheat, rice and soybean, respec-

tively, have experienced yield loss due to warming (i.e., TRI

<

0). For

most of the cropland that has already benefited from warming, i.e., with

original TRI

>

0, SOC improvement has only minimal effects on yield

resilience, especially for wheat and rice.

3.3. Building SOC to secure future food production

Under future climate change, temperature will continue to increase

and crop yields are expected to decrease. Over the growing seasons, the

average temperature would increase by 0.18

–

0.21

◦

C, and 1.18

–

1.44

◦

C

in 2050 under RCP2.6 and RCP8.5, respectively (

and Fig. S7).

The negative impacts of warming on crop yield would be amplified in a

warmer future in most cases, which would inevitably lead to a reduction

of total production (

).

Under existing SOC level, warming leads to global scale loss of yield

in 2050 relative to current level, with 0.2

–

1.0% and 5.2

–

12.4% loss

under RCP2.6 and RCP8.5, respectively. In particular, maize suffers

from the largest yield loss under RCP8.6, reaching 686 kg ha

−

1

, or

12.4% of the current yield (

). The largest loss of production can

also be seen for maize, at about 130 million tonnes each year. Relative to

the scenarios under current SOC level, SOC improvement could largely

avoid the loss of global yield and production. For wheat and rice in

certain cases, warming might even result in slight yield gain in terms of

global average. Specifically, maize yield loss can be minimized to 454

kg ha

−

1

with SOC improvement, avoiding a total production loss of 44.2

million tonnes relative to the scenario with existing SOC. Wheat would

be less affected by future warming with minimum yield loss (

−

175 kg

ha

−

1

), and benefit most from SOC improvement (

). Rice and

soybean yield could be reduced by 7.0% (322 kg ha

−

1

) and 10.6% (281

kg ha

−

1

) under RCP8.5, and the corresponding production loss could be

reduced by 30.2 and 18.4 million tonnes through SOC improvement,

respectively. With global efforts to enrich soil carbon, food systems are

predicted to provide additional 20.0 and 135.7 million tonnes of food

that would otherwise lose due to warming (

), which would be

enough to feed an additional 74.9 and 508.6 million people in terms of

calorie need under RCP2.6 and RCP8.5, respectively.

°

Fig. 2.

The temperature response indices (TRIs) vary with soil properties.

a

to

f

, centered individual conditional expectation (c-ICE) plot of TRI by six soil properties.

Red, blue, green and purple lines represent the averaged TRI changes of maize, wheat, rice and soybean with starting point fixed at zero, respectively, with shadow

indicating the distribution of all individual instances.

g

, the importance of soil properties, sorted from high to low according to the model outputs. Variable

importance was measured by the percentage increase in mean squared error when permuting the variable.

X. Deng et al.

Science of the Total Environment 903 (2023) 166711

6

From a regional perspective, both yield and production would

benefit from SOC improvement regardless of future climate, but the

benefits of avoided loss vary largely by crop and by continent (Fig. S8

and

). With warmer climate (i.e., RCP8.5 relative to RCP2.6), the

overall crop productivity would be more adversely affected (

),

and SOC improvement helps to avoid losses at a much higher magnitude

across the world (Fig. S8 and

). The maize yield and production loss

due to warming of major producers would be significantly reduced

through SOC improvement (

a), e.g., 8.1 million tonnes in South

America and 13.3 million tonnes in North America. For wheat, wide-

spread yield loss in Asia and Europe, the main producing regions, would

be reduced with SOC improvement, avoiding 21.9 and 9.6 million

tonnes production loss, respectively (

b). For rice and soybean that

are primarily produced in one continent, the avoided loss of production

under RCP8.5 can reach 27.5 and 15.5 million tonnes in Asia (

c)

and South America (

d), respectively. Improving SOC reduces a

total production loss in these two regions equaling to 4.1% and 9.6% of

current rice and soybean production, respectively.

Asia would benefit the most from SOC improvement, with an avoi-

ded production loss of 58.1 million tonnes under RCP8.5 in 2050, ac-

counting for 4.3% of the total current production in Asia (

). Among

the four crops in Asia, wheat and rice contribute to over 85% the total

avoided production loss (

b and

4

c). For Africa in particular, the

share of avoided production loss is relatively small, with 14.0 million

tonnes out of 135.7 million tonnes of global total. However, in all cases,

except for rice that is not widely produced, the size of avoided loss

relative to current production is above global average, especially for

maize and wheat (

and Fig. S8). The total avoided loss under

RCP8.5 in Africa account for 10.0% of its current total production, much

higher than that of other continents or global average. This suggests that

for developing regions with high prevalence of undernourishment (e.g.,

African areas), SOC-based strategy could be particularly important for

securing food production under climate change.

Fig. 3.

The estimated global TRIs (%

◦

C

−

1

) of four crops with SOC improvement. (

a

) maize, (

b

) wheat, (

c

) rice and (

d

) soybean. The box plots and the curve on the

left show the frequency distribution of TRI at global scale. Orange and green boxes represent the overall results without and with SOC improvement, respectively.

Green boxes at the bottom show the frequency distribution of TRI of six continents, Africa (AF), South America (SA), North America (NA), Oceania (OA), Asia (AS)

and Europe (EU), and the blank indicates insufficient data in Oceania (

b

to

d

).

Table 1

Changes in global temperature, crop yield and production in 2050 under two climate scenarios.

Crops

Temperature increase

(

◦

C)

Yield change

a

under current SOC (kg

ha

−

1

)

Yield change

a

under improved SOC (kg

ha

−

1

)

Avoided production loss with SOC improvement

b

(million tonnes)

RCP2.6

RCP8.5

RCP2.6

RCP8.5

RCP2.6

RCP8.5

RCP2.6

RCP8.5

Maize

0.19

1.42

−

40.4

−

686.2

−

10.0

−

453.8

5.8

44.2

Wheat

0.21

1.44

−

16.6

−

174.5

18.8

52.4

7.7

49.6

Rice

0.18

1.18

−

10.0

−

321.5

18.2

−

135.2

4.6

30.2

Soybean 0.20

1.39

−

27.7

−

280.5

−

3.1

−

124.4

2.9

18.4

a

Change of yield in 2050 relative to current level (averaged yield from 2011 to 2020).

b

Production change under improved SOC minus production change under current SOC.

X. Deng et al.

Science of the Total Environment 903 (2023) 166711

7

4. Discussion

4.1. Crop yield loss and yield resilience to climate warming

This study specifically investigated the partial crop yield response to

warming by excluding other factors (e.g., precipitation, crop variety,

management), showing relatively comparable findings with other rele-

vant studies. Globally, rising temperature caused maize, wheat and

soybean yield losses on average, yet with spatial divergence (

). For

instance, crops in high-latitude regions would benefit from climate

warming due to longer growing seasons (

J

¨

agermeyr et al., 2021

). Rice

yield was less affected by rising temperature compared with the other

three major crops, consistent with previous meta-analyses and statistical

modeling (

Hasegawa et al., 2022

Lobell et al., 2011

). A larger area of

irrigated rice in the main producing country may bridge the water deficit

associated with warming (

J

¨

agermeyr et al., 2016

). However, a

multimethod-ensemble simulation showed more pessimistic response of

rice to warming (

Zhao et al., 2017

). Note that current TRI of global

average warming for maize and wheat was smaller than that found in

previous studies (

Lobell et al., 2011

Zhao et al., 2017

). A major reason is

that we further isolated the effects of nitrogen and phosphate fertilizer

application. Fertilizer additions can meet possible higher nutrient re-

quirements to maintain crop yield due to reduced nitrogen use efficiency

under climate change (

Vu et al., 2020

;

Woo et al., 2020

). For future

projection, the TRI of four crops in 2050 under RCP8.5, estimated in this

study, is consistent with a recent study that combined field warming

experiments with gridded global crop models (

Wang et al., 2020

). With

SOC improvement, the yield losses due to warming are predicted to be

reduced or even reversed, and therefore the food demands of tens to

hundreds of millions of people worldwide would be met. The relative

production change is mainly affected by TRI (without or with SOC

improvement) and

Δ

T (Eq.

(4)

). Given the greater warming under

RCP8.5, the production increases due to soil improvement would be

more pronounced than that under RCP2.6 (

While there have been studies investigating the relationships be-

tween crop yield and climate factors, there has been a lack of field ev-

idence to isolate the role of soil in building yield resilience to climate

warming at regional to national and global scales (

However, some existing understanding and evidence can still imply the

importance of soil system. A recent report from on-farm trials in China

suggests that high-quality soils can reduce the sensitivity of crop yield to

climate variability and stabilize crop yield (

). Compared

with soils of low quality, high-quality soils are proven to improve yield

under climate change by an average of 1.7% (

). This

study provides field evidence to our findings at the global scale that soil

improvement can increase resilience of the soil-crop system to climate

change (i.e., soil resilience, crop resilience and resilience of the inte-

grated system) and help secure future crop production. Globally, the

benefits of increased SOC are particularly pronounced in wheat cropping

systems (

), and this negative-to-positive effects of improved soil

on yield also appeared in regional cases (

). More

importantly, our study indicates that for regions that are more suscep-

tible to warming, increasing SOC would lead to greater yield resilience.

For instance, in Africa, that has the highest prevalence of undernour-

ishment (19% in 2018

–

2020) (

FAOSTAT, 2021

), the TRI of maize and

soybean can be increased by ~7%

◦

C

−

1

with SOC improvement,

doubling the global average. In these warmer and less irrigated areas,

increasing SOC would prominently alleviate the heat stress on crops

(Fig. S6 and Fig. S9). However, SOC above 2% would not result in

additional benefits to crop yields (

agermeyr and Frieler, 2018

). The threshold effects of SOC

were also detected in field experiments (

Oldfield et al., 2020

). Currently,

SOC content is below 1.5% in two-thirds of planting grids (Fig. S4),

Fig. 4.

Avoided yield and production loss with SOC improvement under RCP8.5 in 2050. (

a

) maize, (

b

) wheat, (

c

) rice and (

d

) soybean. The grid cells show avoided

yield loss after SOC improvement, with gray indicating no significant effect. Blue bars at the bottom show reduced production loss in six continents, Africa (AF),

South America (SA), North America (NA), Oceania (OA), Asia (AS) and Europe (EU), and the blank indicates insufficient data in Oceania. Orange dots in each panel

represent the percentage of avoided production loss in the current total production, and the dash line indicates the global average percentage.

X. Deng et al.

Science of the Total Environment 903 (2023) 166711

8

which leaves great potential to stabilize crop yield under warming by

improving SOC. Our findings highlight the role of soil in improving

overall system resilience to warming and reducing yield loss under

future climate at regional and global scales.

4.2. Potential mechanism of enhancing yield resilience to warming with

improved SOC

Notably, the mechanisms by which improved soil reduces the climate

impact on crops are not fully known. Soil health improvement by

increasing SOC can increase crop yield resilience under extreme climate

stress (

), which is necessary to

support food security under climate change at regional and global scales.

Long-term experiments in China revealed that higher SOC stocks can

reduce annual variation of wheat and maize yield (

Xu et al., 2019

),

especially in Northeast China where has the highest SOC content (

Zhang

et al., 2016

). Specifically, SOC underpins soil structure, soil formation,

water cycling and nutrient cycling (

Taylor et al., 2021

). Poor soil

structure (e.g., soil compaction) lowers root biomass. Increasing SOC

concentration was shown to increase porosity across different soil tex-

tures (

Fukumasu et al., 2022

), which promotes root growth, and nutrient

and water uptake of crops, making them more resilient e.g. to drought

(

Song et al., 2015

). Increased organic matter can increase soil water

holding capacity, thereby alleviating damage of heat and drought and

increasing resilience of maize (

Williams et al., 2016

). The crop is less

sensitive to heat in medium- and fine- textured and carbon rich soils,

partially due to restricted water loss through evapotranspiration (

Huang

et al., 2021

). In this study, compared with wheat and soybean, maize

and rice would benefit less from improving SOC, probably because

maize as a C4 plant has a smaller stomatal conductance to concentrate

CO

2

), and rice is often

irrigated and less water-stressed. Field experiments have shown that rice

could benefit from a higher temperature when soil nutrients keep up

with the demand (

Vu et al., 2020

). Given that higher crop biomass

returns more C into soil (

Wang et al., 2017

), the interaction between

yield and SOC increase presents a positive feedback (

Oldfield et al.,

2020

). On the other hand, losses of SOC and TN, which were pre-

simulated by a process-based model under 3

◦

C of warming, would

reduce wheat yield by 13% and maize yield by 19% (

Basso et al., 2018

).

However, few studies have achieved a separation of the feedback on the

interaction between crop yield resilience to warming and soil properties,

primarily because multiple factors and complex processes are involved,

and the role of soil cannot easily be isolated in the overall yield resilience

observations. It is expected that the relationship between TRI and soil

could be revealed by paired warming experiments across a range of

crop-soil-environment conditions.

It should also be noted that the capacity for building SOC to improve

yield resilience may be limited in certain regions (Fig. S6), and man-

agement practices should be examined for potential trade-offs. Due to

the increase of soil water retention, negative effects of increasing SOC on

maize, wheat and soybean may occur in wet regions with poorer

drainage (

Huang et al., 2021

). The increase of SOC also significantly

increases the specific heat capacity of the soil (

Wang et al., 2019

),

slowing soil warming and drying in spring, and delaying crop planting

and seed germination (

Huang et al., 2021

). The areas with greater

benefits after improving SOC could be given higher priority in regional

or national planning (

Figs. 1, 3

and S6). For areas with higher poverty

and undernourishment, smallholders may not be able to afford costly

measures (

Bryan et al., 2013

), so effective economic and policy in-

centives would need to be in place (

). Food security and other benefits, including ecosystem service and

negative emissions (

Adhikari and Hartemink, 2016

;

Griscom et al.,

2017

), can further justify government investment. Fast and effective

action is required globally (

Paustian et al., 2016

Qin et al., 2021

)

.

4.3. SOC sequestration potential

SOC can be sequestered in croplands, depending on organic matter

inputs (e.g., residue, manure), and other management practices, but

with an upper limit (

Qin et al., 2013

;

Stewart et al., 2008

). Soil man-

agement should also well reflect the level of confidence in both science

and practice. The potential SOC in our study considered a management

scenario with cover cropping, manure application and conservation

tillage, that would be higher than the potential based on the meta-

analysis with applicable constraints (

Lessmann et al., 2021

Compared with the

“

4p1000

”

initiative, potential SOC was simulated

with a relatively conservative sequestration rate, reaching 26% of the

“

4p1000

”

target (

Zomer et al., 2017

). SOC losses due to warming were

not specifically considered in this estimation (

Bruni et al., 2021

Regarding the regional sequestration potential, long-standing cropping

regions in Europe (2.6%), North America (2.4%) and Asia (1.6%) show a

higher rate (Fig. S4), which is reflecting previous carbon depletion due

to intensive land use, leaving more room for carbon accumulation

(

Georgiou et al., 2022

Sanderman et al., 2017

). From a technical

perspective, increasing organic inputs (e.g., crop residue, cover crop and

manure) is considered as the most effective measure to accumulate SOC

in cropland (

Fujisaki et al., 2018

org/10.1016/j.scitotenv.2023.166711

Smith et al., 2020

). Crop residues

return is a feasible and efficient way to increase SOC by 0.69 Mg C ha

−

1

yr

−

1

under a high retention rate (

Wang et al., 2017

). Irrigation of arid

and semiarid regions may also increase SOC through increased biomass

production (

Trost et al., 2013

). Optimal agricultural management in

China was estimated to be able to sequester 2.4 Pg C into cropland

before 2050, with higher potential for paddy soil (26.1 Mg C ha

−

1

) (

). Notably, soil N

2

O emissions may change as

a result of management change aiming at increasing SOC, which should

be further studied for optimizing crop resilience and emission mitigation

(

Lugato et al., 2018

).

4.4. Limitations and uncertainties

Agricultural production involves complex field managements and

multifactorial interactions. Modeling uncertainties may arise from data

limitation (e.g., lack of fine planting regimes and crop calendar at global

scale, relatively short temporal coverage), choice of future GHG emis-

sion scenarios, climate model projections, simplified future crop pro-

duction computation, and understanding of soil-yield resilience

mechanisms. For instance, although precipitation change was included

in our modeling analysis, no significant trends were detected. The lack of

irrigation in the model, due to data limitation, may have partially missed

the water impacts. If crop-specific irrigation data with high spatio-

temporal resolution become available, the cooling and water supply

effects of irrigation could be better modeled (

). Spatially referenced and crop-specific data on fertilization, if

they become available, could also help improve model simulations.

Additionally, since TRI is a simplification of the actual response of crop

to temperature change, future studies could further include biophysical

processes to better understand crop-soil-environment interactions

(

Taylor et al., 2021

). Furthermore, socioeconomic drivers of food supply

and demand besides domestic production of crops, e.g. trade (

Hasegawa

et al., 2021

), are important to assess hunger and the food secure popu-

lation. Finally, acting on soil may lead to other unintended negative

environmental (e.g., water, nutrients input), social (e.g., competitive use

of resources) and even economic outcomes (e.g., shift of investment),

and these should be avoided to the greatest extent possible (

;

;

). Given the mul-

tiple benefits of building SOC, the priority should be given to take effi-

cient management steps considering the integrated crop-soil-

environment system to close the yield gap, ensure the security of food

supply and investigate potential unintended consequences.

X. Deng et al.

Science of the Total Environment 903 (2023) 166711

9

5. Conclusion

A global assessment of maize, wheat, rice and soybean yield change

under climate warming and corresponding crop resilience from soil

improvement was conducted using statistical and random forest

methods. The analyses presented herein showed that each

◦

C of warming

resulted in a 3.4%, 2.4%, 0.3% and 5.0% loss in maize, wheat, rice and

soybean yield, respectively. However, there is spatial heterogeneity in

the yield response to warming (TRI). Among the soil properties, SOC is

the dominant factor that accounts for the spatial disparities of TRI. After

considering the potential of SOC improvement under medium carbon

sequestration scenario, 3%

–

5%

◦

C

−

1

of warming-induced yield loss

would be avoided for four crops. Among the regions with yield loss,

77%

–

96% would benefit from SOC improvement. The increase in SOC

can enhance crop yield resilience to warming and avoid warming

induced crop production loss, providing a soil-based solution for

regional and global food security.

CRediT authorship contribution statement

Z. Q. and Y. H. conceived and designed the research. X. D. and Z. Q.

developed data-model simulations and analyzed results, with key inputs

from Y. H., W. Y. and W. Z. on model improvement. P. C., W. D. and P. S.

helped with interpretation of the results and discussion. X. D., Y. H. and

Z. Q. wrote the manuscript with contributions from all authors.

Declaration of competing interest

The authors declare that they have no known competing financial

interests or personal relationships that could have appeared to influence

the work reported in this paper.

Data availability

All the source data of this study are freely available online and

referenced within the paper. The summary of the dataset is included in

the Supplementary data. The code used to perform analyses in this study

is generated in Python36 and is available upon reasonable request.

Acknowledgements

We thank Yifan Xu for reviewing the algorithms for estimating yield

reponse. This work was supported by the National Natural Science

Foundation of China (41975113, 42141020), the Guangdong Provincial

Department of Science and Technology (2019ZT08G090), and China

Postdoctoral Science Foundation (2021M693583).

Appendix A. Supplementary data

Supplementary data to this article can be found online at

https://doi.

https://doi.org/10.1016/j.geoderma.2015.08.009

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