Couëdel et al. (2026). Beyond Climate Change: The Role of Integrated Soil Fertility Management for Sustaining Future Maize Yield in Sub‐Saharan Africa

; Giller

). Food insecurity in the region has been

exacerbated by recent geopolitical conflicts (Behnassi and El

Haiba

), but is largely due to low soil fertility (Sanchez

2002

Vanlauwe et al.

2015

), inadequate nutrient inputs (Jayne

and Sanchez

), insufficient or erratic rainfall (Kamali

et al.

; Bouhenache et al.

2025

), and pathogen and pest pres-

sures (Savary et al.

), all leading to low crop yields. Maize

productivity plays a crucial role in ensuring food security in SSA,

as it is the most widely grown cereal and the main source of di-

etary energy, providing at least 30% of the total caloric intake of

the region's population (OECD

; Ekpa et al.

). Since the

beginning of the 21st century, the area dedicated to maize cul-

tivation in SSA has nearly doubled (FAOSTAT

), currently

occupying about 40% of the total area cropped with cereals. Yet,

average maize yields across the continent have largely stagnated

at about 1.9 t ha

−

1

(FAOSTAT

), although some countries ex-

hibit modest increases, such as Ethiopia, Angola, Mozambique,

Ivory Coast, and parts of Nigeria, while others experience yield

2 of 15

Global Change Biology,

2026

declines (Ray et al.

). The projected 2.3- fold increase in

maize demand by 2050 (Robinson et al.

2015

) is expected to put

further pressure on food security and land resources.

The urgent need for increased food production contrasts sharply

with current trajectories of environmental factors affecting

crop productivity in SSA. On one hand, rising temperatures

across SSA and decreasing rainfall in West and Southern Africa

(Trancoso et al.

), together with the increasing frequency

of extreme weather events (Westra et al.

; Lesk et al.

Ranasinghe et al.

; Reed et al.

), are expected to

negatively impact maize yields (Sultan and Gaetani

Jägermeyr et al.

), thereby worsening food insecurity

(Tadross et al.

; Falconnier et al.

; Carr et al.

; Ren

et al.

). On the other hand, soil fertility continues to decline

in many regions of SSA due to the low use of mineral fertilizers

and organic inputs, along with the shortened fallow periods and

widespread soil erosion (Vanlauwe and Giller

2006

; Vanlauwe

et al.

2015

; Kihara et al.

; Falconnier et al.

). Evidence

from long- term field experiments shows that low fertilizer in-

puts lead to a progressive decline in both soil fertility and crop

yields over time (Adams et al.

; Cardinael et al.

; Laub,

Corbeels, Mathu Ndungu, et al.

; Laub, Corbeels, Couëdel,

et al.

). Therefore, if soil fertility continues to decline, maize

productivity in SSA is likely to decrease even further.

Integrated soil fertility management (ISFM), which includes

the combined use of mineral and organic inputs, offers the

prospect of increasing crop yield and soil fertility (Vanlauwe

et al.

2010

), thereby improving resilience to climate change

(Gram et al.

). However, due to the difficulty of conduct-

ing climate manipulation experiments, the effectiveness of the

combined use of mineral and organic inputs in the context of

climate change remains insufficiently evaluated, leaving uncer-

tainty about whether its benefits are maintained in changing cli-

matic conditions. In this context, an ensemble of process- based

soil- crop models can help assess the impacts of climate change

(Falconnier et al.

), as they can account for the complex,

nonlinear interactions between soil organic carbon (SOC) dy-

namics and crop growth (Couëdel et al.

; Couëdel, Laub,

et al.

2026

By reinitializing soil conditions at the start of each simulated

year, most crop modeling studies assessing the impact of future

climate change on crop productivity have focused on evaluating

the direct effects of changing temperatures, rainfall, and atmo-

spheric CO

2

concentrations on crop growth and yield (Challinor

et al.

; Falconnier et al.

; Jägermeyr et al.

; Rezaei

et al.

). These studies ignored possible soil fertility decline

over time, thus overlooking the combined effects of climate

change and soil degradation on crop yields. Simulation studies

that incorporate soil fertility dynamics are particularly import-

ant in tropical low- input cropping systems, where soil organic

matter mineralization is the primary source of nutrients for crop

growth (Giller et al.

).

The aim of our study was to assess how long- term changes in

soil fertility and climate drivers affect maize yield in SSA, how

these factors interact, and to what extent ISFM can alleviate

the negative effects. SOC was used as a proxy for soil fertility,

given its central role in determining soil chemical, physical, and

biological properties (Lehmann et al.

). We simulated key

components of climate change affecting crop growth, namely

atmospheric CO

2

, temperature and precipitation, using an en-

semble of 15 process- based soil- crop models. The models were

calibrated using data from four long- term field experiments,

covering diverse representative climates and soils in SSA (see

Figure

). Such long- term experiments remain scarce in the re-

gion, making them particularly valuable for model calibration

and scenario analysis. The study evaluated scenarios of soil fer-

tility change, climate change impacts, and their combined ef-

fects under two contrasting crop management options: (i) no use

of mineral or organic inputs, representing current smallholder

farmers' practices, and (ii) combined use of mineral (80 kg N ha

−

1

season

−

1

) and organic (2 t C ha

−

1

yr

−

1

) inputs, a core principle

of ISFM, representing a plausible sustainable intensification

scenario with nutrient levels required to achieve food security

across SSA (Falconnier et al.

). We hypothesized that (i)

soil fertility decline has a stronger impact on maize yields than

climate change alone, though with varying magnitudes across

sites, and (ii) ISFM mitigates the negative effects of soil fertil-

ity decline, although its effectiveness is reduced under climate

change.

2 | Materials and Methods

2.1 | Multi- Model Ensemble and Model

Calibration Methods

An ensemble of 15 calibrated process- based soil- crop models

was used in this study (see Table

and Couëdel et al. (

) for

detailed model descriptions). In brief, the models differ in how

they simulate soil processes, crop development, crop growth,

and grain yield formation. SOC dynamics are generally sim-

ulated using first- order decay kinetics, but models vary in the

number and type of SOM pools, with some models differenti-

ating active, slow, and passive pools (e.g., DSSAT), while others

use a single- pool approach (e.g., CELSIUS). Ten of the 15 mod-

els include an explicit soil microbial biomass pool with first-

order decay. The representation of fresh organic matter inputs

(crop residues, organic amendments) ranges from a single pool

to multiple specialized pools, as for example implemented in

SIMPLACE. All models simulate soil nitrogen (N) cycling cou-

pled with the SOM pools. Models also differ in their treatment

of other soil processes such as soil water dynamics and nitrate

leaching, while crop growth processes vary in their simulation

of leaf area development, light interception, biomass accumula-

tion, and grain formation. A single experienced modeling team

conducted the simulations with each model. Different versions

of a given model (i.e., DSSAT, Expert- N, and SIMPLACE) were

conducted by the team working with that specific version of

the model.

Model calibration and evaluation under current conditions

were deemed essential for ensuring plausible maize yield sim-

ulations under the future climate change scenarios, while ac-

counting for long- term SOC dynamics and their interactions

with maize growth. The median of the model ensemble (15

soil- crop models) was used to evaluate how well the ensem-

ble reproduced maize yields and SOC dynamics over time

across the four long- term experiments. The modeling teams

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3 of 15

Global Change Biology,

2026

were free to use their own methods for calibration of the crop

and soil modules of their respective model versions. Values for

model input parameters and variables (observed or estimated),

including soil characteristics, initial soil conditions, weather,

soil and crop management, were provided to the modeling

teams for conducting simulations of maize growth during the

years of the experiments at the four sites. Observed data on

maize phenology, grain yield, aboveground biomass, and SOC

were used for model calibration. Couëdel et al. (

) provide

more detailed information about model structural differences

FIGURE 1

| Harvested areas of maize in sub- Saharan Africa showing the four study sites representativeness of the six CIMMYT maize mega en-

vironments (a) and their baseline (1980–2010) mean annual temperature, mean annual precipitation, and clay content (b). Soil organic carbon (SOC)
is the initial SOC concentration (%) (0–20 cm). Map lines delineate study areas and do not necessarily depict accepted national boundaries.

TABLE 1

| The 15 soil- crop process- based models used in the model intercomparison study.

Model

Model references

APSIM v7.9

Holzworth et al. (

CELSIUS

Ricome et al. (

)

DayCent

Del Grosso et al. (

2001

)

DNDC v.CAN

Smith et al. (

DSSAT v4.8.0.19- CERES- Maize + Century

Ritchie et al. (

1998

), Gijsman et al. (

2002

),

Hoogenboom et al. (

)

DSSAT v4.8.0.19- CERES- Maize + Ceres- SOM

Godwin and Jones (

1991

), Hoogenboom et al. (

)

EPIC

Izaurralde et al. (

2006

Expert- N v5.1- Gecros

Biernath et al. (

Expert- N v5.1- Spass

Biernath et al. (

Expert- N v5.1- Ceres

Biernath et al. (

MONICA v3.3.1

Aiteew et al. (

), Nendel et al. (

)

SALUS

Basso et al. (

2010

SIMPLACE- Lintul + Option 1

Enders et al. (

), Gaiser et al. (

)

SIMPLACE- Lintul + Option 2

Faye et al. (

)

STICS v10

Brisson et al. (

2003

), Beaudoin et al. (

)

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4 of 15

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2026

and calibration procedures. In summary, model calibration

consisted of adjusting maize phenology and key crop and

soil parameters to reproduce observed yield, aboveground

biomass, and SOC. The initialization and turnover of soil or-

ganic matter pools were calibrated using observed SOC lev-

els, spin- up model runs, or empirical relationships with soil

texture, depending on the model, with each team applying its

standard procedure to improve the agreement between simu-

lated and observed SOC dynamics.

Model calibration inputs and outputs are available in the CIRAD

repository (Couëdel, Falconnier, et al.

2026

2.2 | Experiments and Data

Data from long- term maize field experiments at four sites in SSA

were used to calibrate 15 process- based soil- crop models (see

Couëdel et al. (

) for experimental details and model descrip-

tions; in the present study InfoCrop was used only for the calibra-

tion exercise and not for the sensitivity analysis to climate change

factors). These sites cover contrasting climates, soil textures,

and soil fertility levels, and are representative of three of the six

maize mega- environments in SSA that are broad agroecologi-

cal zones that share similar climatic, soil, and biotic conditions,

which together determine how maize performs and adapts in a

given region (Hartkamp et al.

2000

; Figure

). The four experi-

ments (Côte d'Ivoire- Gagnoa, Kenya- Embu, Kenya- Machanga,

and Zimbabwe- Murewa) spanned between 9 and 18 years and

were managed by researchers either on- station or on- farm. Such

long- duration field experiments are scarce in SSA, making them

particularly valuable for model calibration and scenario analy-

sis. All sites, except Zimbabwe- Murewa, had two maize growing

seasons per year. The experiments were rainfed and included the

following two treatments: (i) a control without exogenous C and

N inputs, and (ii) a combined application of mineral N (between

100 and 160 kg N ha

−

1

per growing season depending on the site)

and organic inputs (between 3 and 4 t C ha

−

1

per year depending

on the site). This latter treatment represents the input practice

in ISFM. Organic input was compost made from crop residues

at Côte d'Ivoire- Gagnoa and farmyard manure at the other sites.

The input of crop residues to the soil was limited to roots, as abo-

veground biomass residues were removed from the experimental

plots. Biotic stresses (weeds, pests, and diseases) were controlled to

minimize their impact on maize yield.

General soil characteristics and initial soil conditions were

determined at the beginning of the experiments. Data on

crop management (tillage, maize cultivar, planting date and

density, and fertilizer application) was available for all sites

and years. Flowering and maturity dates of the maize cul-

tivars were recorded for Côte d'Ivoire- Gagnoa and Kenya-

Embu, while only cultivar maturity periods were available for

Zimbabwe- Murewa and Kenya- Machanga. Grain yield data

was available for all years and sites, and total aboveground

biomass data was available for all sites except Zimbabwe-

Murewa. SOC data were collected from measurements at

multiple time intervals throughout the experiments on topsoil

samples (0–15 cm at Kenya- Machanga and Kenya- Embu, and

0–20 cm at Zimbabwe- Murewa and Côte d'Ivoire- Gagnoa; see

Figure

2

Weather data during experimental years were obtained pri-

marily from onsite records (rainfall at all sites, minimum and

maximum temperatures at all sites except Zimbabwe- Murewa).

For daily incident solar radiation and relative humidity, and for

temperature data at Zimbabwe- Murewa, records from NASA's

Prediction Of Worldwide Energy Resources (POWER) database

(Duarte and Sentelhas

) were used.

More information about the experiments is provided in ear-

lier publications, for Côte d'Ivoire- Gagnoa (Pichot et al.

Guibert

; Cardinael et al.

), for the two sites in Kenya

(Chivenge et al.

,

; Gentile et al.

; Laub, Corbeels,

Mathu Ndungu, et al.

; Laub, Corbeels, Couëdel, et al.

),

and for the site in Zimbabwe (Zingore et al.

2007

; Rusinamhodzi

et al.

2.3 | Model Simulation Scenarios

The response of the model ensemble to the climate change factors

of changing temperature, rainfall and atmospheric CO

2

was as-

sessed for two crop management scenarios: (i) without external

C and N inputs and (ii) with combined mineral and organic in-

puts, representing ISFM. The model simulations for the 30- year

baseline climate (1980–2010) were done with uniform maize

management practices across all years (Table

S1

). The 30- year

baseline climate data (1980–2010) for rainfall and temperature

were derived from onsite records, supplemented with bias-

corrected NASA POWER data when necessary. NASA POWER

also provided the data for daily incident solar radiation and rela-

tive humidity for the 30- year baseline climate. The ISFM system

combined application of mineral N fertilizer (80 kg N ha

−

1

per

growing season) and organic input (2 t C ha

−

1

year

−

1

), as manure

at all sites except Côte d'Ivoire- Gagnoa where the addition of

compost made from crop residues was simulated. This scenario

represents a plausible sustainable intensification pathway with

nutrient inputs sufficient to meet food security needs across sub-

Saharan Africa (Falconnier et al.

).

Following the AgMIP and C3MP protocols (Ruane et al.

Rosenzweig et al.

), we applied individual changes of tempera-

ture, rainfall and atmospheric CO

2

to assess model responses. This

approach isolates crop model sensitivities to each climate driver,

allowing for direct comparisons across models and sites while

avoiding confounding effects from co- varying changes in climate

scenarios. The response to increasing atmospheric CO

2

was an-

alyzed using concentrations of 360, 450, 540 and 720 ppm. The

response to warmer temperatures was analyzed by uniformly in-

creasing daily minimum and maximum temperatures by respec-

tively 2°C, 4°C and 6°C, relative to the baseline climate. Finally,

the response to change in rainfall was analyzed by decreasing

daily rainfall by 25% and 50%, or by increasing it by 25% and 50%.

The changes in CO

2

, temperature and rainfall were assessed in-

dependently, and factorial combinations were not considered to

isolate the individual effects of change in climate variables. This

approach avoids additional uncertainty, as current models are not

yet fully equipped to reliably simulate complex interactions among

multiple concurrent climate stresses (Webber et al.

In the scenario without C and N inputs, both reset and contin-

uous model simulations were performed to isolate the effects

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FIGURE 2

| Observed (in red) and simulated (in black) maize yield and soil organic carbon (SOC) change for no input (a) and mineral and organic

fertilizers (b) treatments across four long- term experiments in sub- Saharan Africa. ‘Year’ represents years from the start of the experiments. Black
data points are medians of 15 soil- crop models. Dark gray areas indicate the 25th to 75th percentile range of the values simulated. Red lines are linear
(for observed yield) and/or exponential regression functions (for observed SOC). Only significant regressions at

p

< 0.05 are displayed.

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6 of 15

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2026

of climate change factors and those of soil fertility dynamics.

In the reset model simulations, SOC, soil total N, soil mineral

N, and soil water were reinitialized each year with the same

starting values, thereby eliminating the cumulative effects of

changes in soil fertility over time. In contrast, in continuous

simulations, the initial values of these variables for each year

were set according to the simulated values at the end of the

previous year. This allowed each model of the ensemble to

simulate the full 30- year period continuously, accounting for

soil- crop feedback effects on crop yield. In the ISFM scenario

with mineral and organic inputs, only continuous simulations

were conducted.

In the main text and figures, we present results only for the

scenarios of +2°C,

−

25% rainfall, +25% rainfall, and 540 ppm

CO

2

, denoted as ‘warmer temperature’, ‘decreasing rain-

fall’, ‘increasing rainfall’, and ‘higher CO

2

’, respectively. The

+2°C and 540 ppm CO

2

scenarios correspond to conditions

projected under a high- emission pathway (SSP3- 7.0) by 2050

(Meinshausen et al.

; Thrasher et al.

). The ±25%

rainfall scenarios were chosen to represent the widespread

uncertainty in rainfall projections across global climate mod-

els, capturing plausible dry and wet future conditions (see

Table

S2

). The simulation results from the other, more extreme

scenarios (±50% rainfall, up to +6°C, up to 720 ppm CO

2

) are

summarized briefly in the “Results” section and provided

in detail in the “

Supporting Information

”. Model simulation

inputs and outputs are available in the CIRAD repository

(Couëdel, Falconnier, et al.

2026

).

2.4 | Data Analysis

Comparing the reset and continuous model simulations in the

scenario without external C and N inputs enabled to quantify

the average relative change in maize yield (%) due to climate

change alone (CC, Figure

S1A

), soil fertility change alone (SF,

Figure

S1B

) and their combined (CCSF, Figure

S1C

) and inter-

active effects (CC × SF), as follows:

where BL

reset

and CC

reset

represent the average simulated maize

yields from a given model within the 15- model ensemble across

the 30- year simulation period under the reset simulation mode.

BL

reset

corresponds to the baseline climate scenario, while

CC

reset

refers to scenarios with changes in climate factors. BL

cont

and CC

cont

represent the average simulated maize yields from a

given model within the 15- model ensemble across the 30- year

simulation period under the continuous simulation mode. BL

cont

corresponds to the baseline climate scenario, while CC

cont

re-

fers to scenarios with changes in climate factors. The interactive

effect (CC × SF) represents the non- additive component of the

combined effect of climate change and soil fertility change on

maize yield, that is, the portion of the yield response that cannot

be explained by the sum of their individual effects. A positive

CC × SF value indicates a synergistic interaction, while a nega-

tive value indicates that one factor partially offsets the effect of

the other.

The differences between maize yield simulations in the scenario

without C and N inputs and that with mineral plus organic in-

puts under continuous simulation mode allowed to compute the

yield response (%) to organic and mineral input, considering

soil- crop feedbacks, as follows:

where

Y

0

and

Y

MOF

are the simulated maize yields from a given

model within the 15- model ensemble across the 30- year simu-

lation period without and with the mineral and organic inputs,

respectively. Results are then shown using the median of the 15

models (Figure

4

).

We assessed the agreement between models by counting the

number of models performing in the same direction for a given

analysis. Following this approach, agreement between mod-

els was categorized into low (< 50% models agreed), medium

(50% < models agreed < 75%), and high (> 75% models agreed)

confidence levels.

The relationships between (i) observed or simulated maize

yield versus time since the start of the experiment (Figure

2

,

Figure

S7

) and (ii) simulated yield change with organic plus

mineral inputs versus time (Figure

4

) were fitted using the lm

function in R (R core team

). For all statistical tests, the sig-

nificance level,

α

, was set at 0.05.

For the scenario without C and N inputs, observed SOC

changes over time (Figure

2a

) were fitted with an exponential

decay function using the EXD.3 function of the ‘drc’ package

in R (R core team

). For the scenario with C and N inputs,

an asymptotic function using the AR.3() function was used

(Figure

2b

3 | Results

3.1 | The Calibrated Soil- Crop Model Ensemble

Reproduced Trends in Maize Yield and SOC

For the treatment without C and N inputs, the ensemble median

accurately reproduced the observed decrease in SOC over time

at the Côte d'Ivoire- Gagnoa and Kenya- Embu sites, but under-

estimated SOC loss at the Kenya- Machanga and Zimbabwe-

Murewa sites (Figure

2a

). Although the models did not fully

capture interannual maize yield variability and tended to over-

estimate yields in the first years at all sites except Zimbabwe,

they reproduced the observed temporal trends reasonably well,

showing declining yields at Côte d'Ivoire- Gagnoa and Kenya-

Machanga, and stable yields at Kenya- Embu and Zimbabwe-

Murewa (Figure

2a

). For the treatment with combined use of

(1)

CC

( % ) =

CC

reset

−

BL

reset

BL

reset

×

100

(2)

SF

( % ) =

BL

cont

−

BL

reset

BL

reset

×

100

(3)

CCSF

( % ) =

CC

cont

−

BL

reset

BL

reset

×

100

(4)

CC

×

SF

( % ) =

CCSF

− (

CC

+

SF

)

(5)

Δ

Y

MOF

( % ) =

Y

MOF

−

Y

0

Y

0

×

100

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FIGURE 3

| Boxplots of simulated maize yield change (%) by climate change only (CC only, see Equation (

) in Section

2.4

), soil fertility declines

only (SF only; i.e., difference between yearly reinitialization of soil conditions vs. continuous model simulation, see Equation (

2

) in Section

2.4

) and

the combination between climate change and soil fertility decline (CC and SF combined, see Equation (

3

) in Section

2.4

) compared to no climate

change and no soil fertility decline scenario (no CC and no SF decline). Simulations represent no fertilizer systems, averaged across 30 years for each
of the 15 individual models. Results are averaged across sites (a) and per site (b).

FIGURE 4

| Simulated maize yield change (%) with mineral and organic inputs (80 kg N ha

−

1

season

−

1

and 2 t C ha

−

1

yr

−

1

organic amendment)

compared to no fertilizer systems under climate change (CC) or not (no CC). Simulations are averaged across sites (a) and trends over years for the
four sites (b). Data are from the median of 15 soil- crop models' simulations. Only significant linear regressions at

p

< 0.05 are displayed. All simula-

tions included long- term soil- crop feedback (continuous model simulation without yearly reinitialization of soil conditions).

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mineral and organic inputs, the ensemble median reproduced

reasonably well the observed stability of SOC levels over time

at Côte d'Ivoire- Gagnoa, but underestimated the SOC decrease

at Kenya- Embu and the SOC increases observed at Zimbabwe-

Murewa and Kenya- Machanga. Despite not fully capturing the

interannual maize yield variability, the ensemble median repro-

duced well the observed stability of yields over time at all four

sites (Figure

2b

3.2 | Soil Fertility Decline Has a Larger Impact on

Maize Yield Than Climate Change Factors

In the maize systems without C and N inputs, the scenario simu-

lations with the model ensemble over the 30- year period suggest

that the decline in soil fertility had a larger impact on yield than

climate change factors (Figure

3a

, high confidence). The average

simulated decrease in maize yield due to soil fertility decline was

24% across the four sites, far outweighing the effects of individ-

ual climate factors: +2% for warmer temperature (+2°C),

−

4%

for reduced rainfall (

−

25%), +8% for increased rainfall (+25%),

and +6% for higher CO

2

levels (540 ppm; Figure

3a

). This greater

impact of declining soil fertility over climate change factors was

consistently simulated across a wider range of changes in tem-

perature (from +2°C to +6°C), rainfall (from

−

50% to +50%),

and CO

2

levels (from 450 to 720 ppm; Figure

S2

). The differences

in maize yield impacts between the soil fertility decline scenario

and the climate change factors scenarios were most pronounced

at Côte d'Ivoire- Gagnoa (for all climate change factors, me-

dium confidence; Figure

), the site with the highest SOC loss

(Figure

S3

). In contrast, the differences were smallest at Kenya-

Embu (medium confidence), the site with the highest initial SOC

and soil clay content.

In the scenarios of climate change alone (i.e., without incor-

porating soil fertility decline in the model simulations, see

Methods), simulated maize yields without C and N inputs de-

creased significantly (

−

10%) under a +2°C temperature increase

at Côte d'Ivoire- Gagnoa, which is the warmest site (Figure

,

high confidence). In contrast, simulated yields increased under

warmer temperatures at Kenya- Embu (Figure

, medium con-

fidence), the site with the coolest baseline climate. This was

likely due to temperatures approaching the optimum thresholds

for maize photosynthesis (Rezaei et al.

), as also reported

for other high- altitude sites in Kenya (Waha et al.

). At

Kenya- Machanga and Zimbabwe- Murewa, a +2°C temperature

increase had no effect on simulated maize yields, although with

low confidence among the models. This uncertainty may arise

from differences in how models simulate the processes driving

yield reductions (shorter growth period and frequency of heat

stresses on for example, photosynthesis, grain number develop-

ment, and grain filling) or yield increases (when temperatures

are nearing optimal thresholds for photosynthesis) (Webber

et al.

). Changes in simulated maize yield due to increased

or reduced rainfall were strongest at the two Kenyan sites

(Figure

). At Zimbabwe- Murewa, simulated yields remained

unchanged under higher rainfall but with low confidence among

the models. This uncertainty could come from differences in

how models simulate nitrate leaching and waterlogging, which

can reduce yields under excessive rainfall (Webber et al.

Kim et al.

).

When comparing the combined effect of soil fertility decline and

climate change factors on maize yield with the sum of their indi-

vidual effects (Figure

3

), it becomes clear that the models simulated

only minor interactive effects, on average and per site. Indirect ef-

fects of climate change factors resulted in yield changes of +2%

for warmer temperatures, +3% for lower rainfall,

−

3% for higher

rainfall, and

−

1% for higher CO

2

levels (Figure

S4a

). These small

interactive effects are largely explained by the minimal impacts of

climate change factors on SOC dynamics in the model simulations

(Figure

S3

, high confidence).

3.3 | Maize Yield Responses to ISFM Increase

Over Time

Under the baseline climate scenario, the combined application of

mineral and organic inputs increased average maize yield across

the four sites by about 182% over the 30- year simulation period

compared to no input use (Figure

4a

, high confidence). The simu-

lated yield response decreased slightly in the scenarios of warmer

temperatures (170% yield increase) and lower rainfall (156%),

but increased under higher rainfall (215%) and higher CO

2

levels

(186%; Figure

4a

). The yield responses were even more pronounced

in scenarios with more severe climate change (e.g., +6°C warm-

ing and

−

50% rainfall resulted in 160% and 95% yield increases,

respectively; Figure

S5

). The relatively small differences in sim-

ulated yield responses between the baseline and climate change

scenarios can be partly attributed to the simulation of small direct

effects of the climate change factors on SOC dynamics, as influ-

enced by input use (

−

5.5% SOC in the baseline versus

−

4.5% to

−

6% in the climate change scenarios), leading to only modest dif-

ferences in maize growth over time (Figure

S6

, high confidence).

At all sites, simulated maize yield responses to inputs increased

over time both in the baseline climate (medium confidence)

and climate change scenarios (except for the reduced rainfall

scenario at Kenya- Embu and Kenya- Machanga; Figure

4b

Simulated yield responses to C and N inputs were larger at Côte

d'Ivoire- Gagnoa, Zimbabwe- Murewa, and Kenya- Machanga,

which are characterized by low initial soil fertility and high SOC

loss without fertilization, than at Kenya- Embu, characterized by

a high initial SOC content (medium confidence). With mineral

and organic inputs, simulated SOC was maintained over time at

Kenya- Machanga and Zimbabwe- Murewa, but slightly decreased

at Côte d'Ivoire- Gagnoa and Kenya- Embu. Despite the SOC de-

cline, absolute maize yield at Côte d'Ivoire- Gagnoa remained

constant, in contrast to the scenario without C and N inputs,

where both SOC and yield declined (Figure

S7

). At Kenya- Embu,

the combined use of mineral and organic inputs was simulated

to have the lowest yield benefit. It was the only site where simu-

lated maize yields decreased significantly over time with C and

N inputs (Figure

S7

).

4 | Discussion

4.1 | Soil Fertility Decline Outweighs Climate

Change Effects on Maize Yield

Our study has important implications for understanding the rel-

ative impact of soil fertility and climate change on maize yields

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9 of 15

Global Change Biology,

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in smallholder cropping systems in SSA, which are typically

characterized by maize monoculture with low input use. To our

knowledge, this is the first study to use calibrated models that

reproduce long- term trends in both soil and crop yield dynamics

to investigate soil–crop feedbacks, apart from a few comparable

modeling studies conducted in temperate conditions (e.g., Smith

et al.

Our model simulations show that if fertilizer inputs remain

low, maize yields in SSA are expected to further decline by

20%–50% over the next three decades, as a result of soil nutri-

ent depletion (notably N, as represented in our study). Soil nu-

trient depletion driven by SOC decline is a major form of soil

degradation in SSA (Kihara et al.

), reducing crop produc-

tivity, biomass, and soil cover (Woomer and Muchena

1996

).

This, in turn, accelerates other degradation processes like

erosion, acidification, and compaction, further reducing soil

health (Lal et al.

1997

); these processes are not accounted

for in the simulations of the study and would require further

model improvement. Poor soil health is linked to low crop pro-

ductivity, food insecurity, and persistent rural poverty across

the continent (Sanchez

2002

).

Confirming our first hypothesis, climate change is expected to

have a relatively smaller impact on maize yields than soil fer-

tility decline, with the contrast in maize yield responses being

strongest at sites with greater SOC loss and weakest at those

with higher initial SOC and clay content. However, climate

change may exacerbate these impacts, with the magnitude

and direction of yield changes varying substantially across

regions and maize mega- environments in SSA. Our model

simulations indicate that rising temperatures and decreasing

rainfall are likely to reduce maize yields in areas located in

West Africa (Côte d'Ivoire- Gagnoa in our study), whereas in-

creasing temperatures combined with higher rainfall are ex-

pected to enhance yields in the highland regions of Eastern

Africa (Kenya- Embu in our study). Lowland and dry mid-

altitude areas may experience greater relative yield reductions

compared to highland and mid- altitude environments (Waha

et al.

; Dale et al.

). Besides, impacts will likely vary

within regions, depending on differences in crop manage-

ment and resource use (e.g., Falconnier et al.

; Siatwiinda

et al.

The interaction between climate change and soil fertility de-

cline, defined as the non- additive component of their com-

bined effects on maize yield, was relatively small. This finding

is notable because soil organic matter is expected to decrease

under warmer climate due to higher mineralization rates

(e.g., Nissan et al.

), potentially leading to negative soil–

crop feedback loops. A stronger influence of climate change

on soil fertility might have been expected through its effects

on mineralization dynamics, yet this was not evident in our

simulations.

4.2 | ISFM as a No- Regret Climate Change

Adaptation Option

African governments have endorsed the 2024 Nairobi

Declaration on Fertilizer and Soil Health, emphasizing the need

to promote agricultural practices that improve soil health, to in-

crease fertilizer use, and to enhance food security across SSA

(African Union

). In line with findings from long- term field

experiments conducted in several countries in SSA (e.g., Adams

et al.

; Cardinael et al.

; Sommer et al.

), our model

simulations show the potential to maintain SOC or slow its loss

and enhance maize productivity through the combined appli-

cation of mineral fertilizer and organic inputs. The simulations

further suggest an increasing yield benefit of mineral and or-

ganic inputs over time due to their accumulating positive effects

on SOC. Combined application of mineral fertilizer and organic

inputs is one of the core principles of ISFM, recognizing that

substantial improvements in crop productivity in SSA require

addressing both the immediate nutrient needs of crops and the

long- term maintenance of soil fertility (Vanlauwe et al.

2010

The combined application of mineral and organic inputs is

often promoted as an effective climate change adaptation

strategy in SSA (Nezomba et al.

; Gram et al.

; Carr

et al.

; Markos et al.

). Our findings support this to

a certain extent, showing that their combined use acts as a

‘true- adaptation’ measure (Lobell

) to increased rainfall,

with yield benefits becoming more pronounced under cli-

mate change in our model simulations. However, the model

ensemble also indicates that the combined use of mineral

and organic inputs, whilst increasing maize yields, does not

mitigate the negative impacts of warmer temperatures and

reduced rainfall. Therefore, application of mineral and or-

ganic inputs can be seen as a ‘no- regret adaptation’, benefi-

cial under current conditions, yet insufficient on its own to

counteract future climatic stresses (Hallegatte

), thereby

confirming the second hypothesis of this study. Finally, it is

also important to note that the no- regret adaptation benefits

of input use under warmer and drier climates could be further

enhanced by reducing crop sensitivity to climate extremes, for

example through the use of heat- and drought- tolerant culti-

vars, longer- cycle cultivars, or adjusted sowing dates (Hunt

et al.

; Hasegawa et al.

). These strategies form an

integral part of the ISFM approach but were not simulated in

our study.

4.3 | Limitations of the Study and Future Research

Directions

In most climate change impact studies conducted to date, soil-

crop models were reinitialized each year with the same initial

soil water, soil N, and SOC levels (e.g., Dale et al.

; Falconnier

et al.

; Waha et al.

). As a result, these studies fail to

account for the shifts in baseline crop productivity caused by

progressive soil fertility loss over time, which our study shows

to be of high importance for low- input cropping systems in

SSA. Only a few studies, all conducted in temperate regions,

have included long- term soil- crop feedback and reported their

significant impact on simulated crop yields (Basso et al.

; Smith et al.

). Here we argue that to accurately assess

the long- term impacts of climate change and the effectiveness

of adaptation strategies, models must be run continuously, in-

corporating soil- crop feedback mechanisms. This is essential to

prevent underestimation of both yield losses due to soil fertility

decline and yield gains from fertilizer application.

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10 of 15

Global Change Biology,

2026

Our study calls for future research to reduce uncertainties in

assessing the impacts of climate change on crop growth and

soil fertility, as displayed by the variability of outputs from our

modeling ensemble. Although our findings suggest that soil

degradation currently exerts a stronger influence on maize yield

than climate change in our study locations, they highlight the

dual challenges of declining soil fertility and increasing climate

threats over the coming decades. Over longer time horizons, cli-

mate change effects are expected to become more pronounced

and will likely emerge as the predominant driver of crop pro-

ductivity (Waha et al.

; Dale et al.

). There are, how-

ever, a number of important limitations to consider in our study

and in most crop simulation modelling of climate change im-

pacts in general (Corbeels et al.

; Kephe et al.

; Wang,

Jägermeyr, et al.

). First, our simulations do not account for

the projected increase in weather variability with more frequent

extreme events. Extreme temperatures causing heat stress in

crops and extreme rainfall leading to drought or flooding are

likely to become more common with climate change (Taylor

et al.

; IPCC

). Some regions in SSA already experi-

ence crop failure rates of 15% or more, and these are projected

to increase significantly in the future (Parkes et al.

). Many

current crop models do not adequately simulate the effects of ex-

treme weather events and this requires further methodological

development (Webber et al.

; Kim et al.

), coupled with

innovative field experiments (for example manipulating rain-

fall in SSA) to better calibrate these processes (Bouhenache

et al.

2025

). Second, experiments show that the combined ef-

fects of heat and drought stress have a more adverse impact

on maize grain yield than the individual stresses alone (Cohen

et al.

; Bheemanahalli et al.

), leading to larger yield

penalties and increased yield variability. Models need further

development and evaluation to account for these interactions

(Webber et al.

). Using individual change in temperature,

precipitation, and CO

2

, our study omits factorial combinations

between climate factors (e.g., CO

2

× temperature or CO

2

× rain-

fall), which may lead to under- or overestimation of combined

effects under real- world conditions. However, this was a deliber-

ate methodological choice, as most current crop–soil models are

not yet well equipped to simulate multiple simultaneous stresses

reliably, and including them would have introduced additional

model uncertainty (Webber et al.

). Recent work has fur-

ther demonstrated the diagnostic value of this approach, show-

ing that modeled yield sensitivities to individual climate factors

differ widely across models (Müller et al.

).

Third, climate change directly impacts soil fertility. Intensive

rainfall events, expected to become more frequent with climate

change (Donat et al.

; Reed et al.

), accelerate soil deg-

radation and erosion (Van Oost et al.

2007

; Borrelli et al.

).

Most soil- crop models either oversimplify or omit soil erosion

processes (Raza et al.

). Accurately assessing the impact of

climate change on soil fertility processes requires incorporating

the relationships between intense rainfall and soil erosion into

models. Moreover, climate extremes, such as heatwaves, are

also expected to have an exacerbated negative impact on SOC

(Anjileli et al.

), further declining soil fertility (Lugato

Wang, Zhang, et al.

). Our model- ensemble simulated rel-

atively small impacts of climate change factors on SOC which

likely led to an underestimation of the negative impacts of cli-

mate change on maize yield. Finally, climate change may also

alter the dynamics of pathogens and pests, important yield-

reducing factors not yet captured by current soil- crop models.

While addressing these biotic interactions is beyond the scope of

this study, it represents a valuable direction for future research

and model development (Savary et al.

https:// www. pepr- fairc arbon. fr/ eng

). A strong engage-

ment of the modeling community is now critically required to

address the above modeling issues on extreme climate events

and conduct climate change impact studies that account for soil

fertility changes, thereby enabling more realistic projections of

climate change impacts on agriculture in SSA.

5 | Conclusion

Over the long term, soil fertility decline can reduce maize yields

more strongly than climate change in SSA, making soil degrada-

tion a priority to address alongside climate adaptation efforts. By

explicitly accounting for soil fertility changes, our findings support

the value of ISFM, through the combined application of organic

and mineral inputs, as an effective long- term no- regret adaptation

strategy to sustain and increase yields under both current and fu-

ture climates. We recommend that assessments of long- term cli-

mate impacts and adaptation strategies require continuous model

simulations that capture soil–crop feedbacks to realistically rep-

resent long- term system dynamics. Finally, improving model rep-

resentation of climate extremes and interacting stressors on crops

and soils will be key to avoid underestimating climate change im-

pacts in smallholder farming systems in sub- Saharan Africa.

Author Contributions
Antoine Couëdel:

conceptualization, data curation, formal analysis,

investigation, methodology, project administration, software, supervi-

sion, validation, visualization, writing – original draft, writing – review

and editing.

Gatien N. Falconnier:

conceptualization, formal analysis,

funding acquisition, investigation, methodology, project administra-

tion, resources, supervision, validation, visualization, writing – original

draft, writing – review and editing.

Myriam Adam:

conceptualization,

investigation, methodology, supervision, validation, visualization, writ-

ing – review and editing.

Rémi Cardinael:

conceptualization, funding

acquisition, investigation, methodology, resources, supervision, valida-

tion, visualization, writing – review and editing.

Johan Six:

conceptu-

alization, data curation, funding acquisition, investigation, methodology,

resources, supervision, validation, writing – review and editing.

Moritz

Laub:

conceptualization, data curation, formal analysis, investigation,

methodology, software, validation, visualization, writing – review and

editing.

Alex C. Ruane:

conceptualization, funding acquisition, method-

ology, project administration, resources, supervision, validation, writing

– review and editing.

Kenneth Boote:

conceptualization, investigation,

methodology, software, validation, visualization, writing – review and

editing.

Eric Justes:

conceptualization, investigation, methodology,

software, validation, writing – review and editing.

Ward N. Smith:

con-

ceptualization, investigation, methodology, software, validation, writing

– review and editing.

Anthony M. Whitbread:

conceptualization, in-

vestigation, methodology, software, validation, writing – review and ed-

iting.

François Affholder:

investigation, software, validation, writing

– review and editing.

Folorunso M. Akinseye:

investigation, software,

validation, writing – review and editing.

Juraj Balkovic:

investigation,

software, validation, writing – review and editing.

Bruno Basso:

inves-

tigation, software, validation, writing – review and editing.

Arti Bhatia:

investigation, software, validation, writing – review and editing.

Bidisha

Chakrabarti:

investigation, software, validation, writing – review and

editing.

Regis Chikowo:

data curation, investigation, resources, valida-

tion, writing – review and editing.

Mathias Christina:

investigation,

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11 of 15

Global Change Biology,

2026

software, validation, writing – review and editing.

Babacar Faye:

in-

vestigation, software, validation, writing – review and editing.

Fabien

Ferchaud:

investigation, software, validation, writing – review and

editing.

Christian Folberth:

investigation, software, validation, writ-

ing – review and editing.

Thomas Gaiser:

investigation, software, val-

idation, writing – review and editing.

Marcelo Galdos:

investigation,

software, validation, writing – review and editing.

Sebastian Gayler:

investigation, software, validation, writing – review and editing.

Aram

Gorooei:

investigation, software, visualization, writing – review and

editing.

Brian Grant:

investigation, software, validation, writing – re-

view and editing.

Hervé Guibert:

data curation, investigation, valida-

tion, writing – review and editing.

Gerrit Hoogenboom:

investigation,

methodology, software, validation, visualization, writing – review and

editing.

Bahareh Kamali:

investigation, software, validation, writing –

review and editing.

Fidel Maureira:

investigation, software, validation,

writing – review and editing.

Fasil Mequanint:

investigation, software,

visualization, writing – review and editing.

Claas Nendel:

investiga-

tion, software, visualization, writing – review and editing.

Cheryl H.

Porter:

investigation, software, validation, writing – review and edit-

ing.

Dominique Ripoche:

investigation, software, validation, writing

– review and editing.

Leonard Rusinamhodzi:

data curation, inves-

tigation, validation, writing – review and editing.

Shikha Sharma:

in-

vestigation, software, validation, writing – review and editing.

Upendra

Singh:

investigation, software, validation, writing – review and ed-

iting.

Amit Srivastava:

investigation, software, validation, writing

– review and editing.

Bernard Vanlauwe:

funding acquisition, investi-

gation, methodology, resources, validation, writing – review and editing.

Antoine Versini:

investigation, software, validation, writing – review

and editing.

Murilo Vianna:

investigation, software, validation, writing

– review and editing.

Heidi Webber:

investigation, software, validation,

writing – review and editing.

Tobias Weber:

investigation, software,

validation, writing – review and editing.

Congmu Zhang:

investigation,

software, validation, writing – review and editing.

Marc Corbeels:

con-

ceptualization, funding acquisition, investigation, methodology, project

administration, resources, supervision, validation, visualization, writing

– original draft, writing – review and editing.

Affiliations

1

AIDA, Univ Montpellier, CIRAD, Montpellier, France |

2

CIRAD,

UPR Aida, Montpellier, France |

3

CIRAD, UPR Aida, Harare,

Zimbabwe |

4

International Maize and Wheat Improvement Centre

(CIMMYT)-

Zimbabwe, Harare, Zimbabwe |

5

Department of Plant

Production Sciences and Technologies, University of Zimbabwe, Harare,

Zimbabwe |

6

AGAP Institut, Univ. Montpellier, INRAE, CIRAD,

Inst. Agro., Montpellier, France |

7

Faculty of Agriculture and Food

Processing, National University of Battambang, Krong Battambang,

Battambang Province, Cambodia |

8

Department of Environmental

Systems Science, ETH Zurich, Zurich, Switzerland |

9

NASA Goddard

Institute for Space Studies, New York, New York, USA |

10

Department

of Agricultural and Biological Engineering, University of Florida,

Gainesville, Florida, USA |

11

PERSYST Department, Univ Montpellier,

CIRAD, Montpellier, France |

12

Ottawa Research and Development

Centre, Agriculture and Agri-

Food Canada, Ottawa, Ontario,

Canada |

13

International Livestock Research Institute, Dar es Salaam,

Tanzania |

14

CIRAD, UPR Aida, Maputo, Mozambique |

15

Faculdade

de Agronomia e Engenharia Florestal, Universidade Eduardo Mondlane,

Maputo, Mozambique |

16

International Crop Research Institute for

the Semi- Arid Tropics (ICRISAT), Thiès, Senegal |

17

Centre d'Etude

Régional pour l'Amélioration de l'Adaptation à la Sécheresse (CERAAS),

Thiès Escale, Senegal |

18

Biodiversity and Natural Resources Program,

International Institute for Applied Systems Analysis, Laxenburg,

Austria |

19

Department of Earth and Environmental Sciences,

Michigan State University, East Lansing, Michigan, USA |

20

ICAR,

Indian Agricultural Research Institute, New Delhi, India |

21

Plant,

Soil and Microbial Sciences Department, Michigan State University,

East Lansing, Michigan, USA |

22

CIRAD, UPR Aida, Saint- Denis,

La Réunion, France |

23

Université du Sine Saloum El Hadj Ibrahima

Niass (USSEIN), Kaolack, Senegal |

24

BioEcoAgro Joint Research

Unit, INRAE, Université de Liège, Université de Lille, Université de

Picardie Jules Verne, Barenton- Bugny, France |

25

UMR Eco&Sols, Univ

Montpellier, CIRAD, INRAE, IRD, InstitutAgro Montpellier, Montpellier,

France |

26

Institute of Crop Science and Resource Conservation,

University of Bonn, Bonn, Germany |

27

Sustainable Soils & Crops,

Rothamsted Research, Harpenden, UK |

28

Biogeophysics, Institute

of Soil Science and Land Evaluation, Hohenheim University, Stuttgart,

Germany |

29

Institute of Bio- and Geosciences, Agrosphere (IBG- 3),

Forschungszentrum Jülich GmbH, Jülich, Germany |

30

CIMMYT, Addis

Ababa, Ethiopia |

31

Leibniz Centre for Agricultural Landscape Research

(ZALF), Müncheberg, Germany |

32

Institute of Biochemistry and

Biology, University of Potsdam, Potsdam, Germany |

33

Global Change

Research Institute CAS, Brno, Czech Republic |

34

AgroClim, INRAE,

Avignon, France |

35

International Institute of Tropical Agriculture,

Accra, Ghana |

36

International Fertilizer Development Center (IFDC),

Muscle Shoals, Alabama, USA |

37

International Institute of Tropical

Agriculture (IITA), Nairobi, Kenya |

38

CIRAD, UPR Recyclage &

Risque, Saint- Denis, La Réunion, France |

39

Faculty of Environment

and Natural Sciences, Brandenburg University of Technology, Cottbus,

Germany |

40

Section of Soil Science and Agrohydrology, University of

Kassel, Kassel, Germany |

41

CIRAD, UPR AIDA, Nairobi, Kenya

Acknowledgements

We are grateful to the support provided by the Agricultural Model

Intercomparison and Improvement Project (AgMIP) leadership team

and those who participated as part of the AgMIP Low Input Farming

Systems Activity. Part of the study was funded by the DSCATT project

“Agricultural Intensification and Dynamics of Soil Carbon Sequestration

in Tropical and Temperate Farming Systems” (N° AF 1802–001, N°

FT C002181), supported by the Agropolis Foundation (“Programme d'

Investissement d' Avenir” Labex Agro, ANR- 10- LABX0001–01) and by

the TOTAL Foundation within a patronage agreement. This study was

also supported by the RAIZ “Promoting agroecological intensification

for resilience building” project FOOD/2021/424–933 (

https:// raiz. org.

zw/

) funded by the European Union, and from the ALAMOD project

(

) of the exploratory research pro-

gram FairCarboN and received government funding managed by the

Agence Nationale de la Recherche under the France 2030 program,

reference ANR- 22- PEXF- 0002. A.C.R. participation was made possible

by NASA Earth Science Division support to the NASA GISS Climate

Impacts Group. We thank the CGIAR Sustainable Farming Program for

their support to this research.

Funding

This work was supported by Agropolis Fondation, ANR-

10- LABX0001- 01.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available at CIRAD

repository:

https:// doi. org/ 10. 18167/ DVN1/ EGJXVL

.

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