CIAT Research brief, December 2019 1
Over the past decades, numerous crop-soil models 
have been developed to represent dynamic processes 
in cropland systems, including soil organic carbon 
(SOC) dynamics (Campbell and Paustian, 2015). These 
models use mathematical equations that determine 
carbon allocation in the vegetation and biomass and 
soils to represent biogeochemical processes, such 
as photosynthesis, respiration and decomposition. 
Furthermore, a range of crop management practices 
are represented in most of the models, enabling an 
assessment of their impacts on SOC in agricultural 
systems. Although models were initially developed for 
research purposes, they are increasingly becoming 
important in many aspects of environmental policies 
(Manlay et al., 2007). Extensively tested models provide 
effective tools that can be used in identifying sustainable 
land management practices across different agro-
ecological conditions. Compared to field experiments, 
which are time and resource consuming, models are 
more effective for making predictions and understanding 
crop and SOC dynamics on large scales and different 
time scales. 
However, the choice of the model depends on the ability 
of the model to simulate key processes in the region of 
interest. We conducted a survey to identify the features 
of the commonly used crop-soil models in order to inform 
the choices for application in sub-Saharan Africa. The 
survey was administered online to the model developers. 
In addition, we also conducted a literature search to 
assess the usage of the different models in different parts 
of sub-Saharan. In this brief, we provide a basic summary 
of the information from the survey and literature review. 
Introduction
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Crop and soil organic matter simulation 
models – A brief review of their basic features 
and application in sub-Saharan Africa  
Sylvia Sarah Nyawira
CIAT Research brief, December 20192
Summary of the basic features
The models considered for the survey included 
point and landscape level that have been applied in 
different in regions in the globe for simulating crop 
and soil organic carbon dynamics. The 9 models that 
participated in the survey are: DayCent/Century, RothC, 
DNDC, DAISY, MONICA, CropSyst, APSIM, DSSAT, and 
STICS. Below a brief description of each of the models. 
i. DayCent is a biogeochemical model that 
simulates crop growth, SOC dynamics, carbon 
and trace gas fluxes in croplands as well as 
forests, grasslands, and savannah ecosystems 
(Del Grosso et al., 2002; Parton et al., 1998). It is 
the daily time step version of the Century model. 
The model allows for an assessment of a wide 
range of agronomic management practices (e.g., 
tillage, fertilization, irrigation, crop harvest and 
manure addition). 
ii. MONICA is the latest generation of HERMES 
(Kersebaum, 1995, 2007) model versions that 
simulates crop growth, water and nitrogen 
uptake, and the SOC dynamics in the soil (Nendel 
et al., 2011).
iii. DAISY is a mechanistic model that represents 
physical and biological processes in agricultural 
fields (Hansen, 2002). It simulates water, energy, 
carbon and nitrogen cycles in the vegetation and 
soils agricultural systems.
iv. RothC is a model for turnover of carbon in non-
water-logged topsoil (Coleman, 2014). It allows 
for the assessment of the effects of soil type, 
temperature, soil moisture and plant cover on 
the turnover processes. The model does not 
represent crop growth and is hence not used in 
modelling crop dynamics. 
v. STICS simulates plant growth, water, carbon and 
nitrogen fluxes in annual crops, perennial grasses 
or trees (Brisson et al., 2003, 1998). 
vi. DSSAT (Decision Support System for 
Agrotechnology Transfer) is a software 
application program that comprises crop 
simulation models for over 42 crops, which 
simulate growth, development and yield as a 
function of the soil-plant-atmosphere dynamics. 
DSSAT simulates water, nitrogen and carbon 
cycles for these crops, and can be used to 
assess the effects of climate change impacts and 
different management decisions (Jones et al., 
2003).
vii. CropSyst is a cropping systems simulation 
model developed as an analytical tool to study 
the effects of climate, soils, and management 
on cropping systems productivity and the 
environment (Stöckle et al., 2003).
viii. APSIM is a comprehensive model developed 
to simulate biophysical to simulate biophysical 
processes in agricultural systems. APSIM includes 
modules that simulate soil processes including 
water balance, N and P transformations, soil pH, 
erosion and a full range of management controls 
in diverse range of crops (Keating et al., 2003). 
ix. DNDC simulates C and N in agro-ecosystems 
and predicts crop growth, SOC dynamics and 
N leaching and trace gases emissions (Li et al., 
2012).
Table 1 provides a summary of the features of the 
model including, the time step, number of layers 
and carbon pools, the extent of application and the 
simulated nutrients and greenhouse gases. The 
responses show that most of the models can simulate 
both crop and soil organic carbon (SOC) dynamics, 
with the exception of RothC which simulates only 
SOC dynamics. Apart from RothC, all the reviewed 
models include a layered soil profile to represent water 
dynamics in the soil. Most of the models use a tipping 
bucket approach for simulating soil hydrologic cycle 
and water redistribution, with the exception of CropSyst 
and Daisy that use the Richard’s equation. Although 
the tipping bucket model has been shown to work well 
in representing soil water holding properties, it has 
less accuracy in estimating soil moisture distribution 
in the soil profile (Shelia et al., 2018). Majority of 
the models can simulate CO2 and N20 fluxes with a 
few also simulating CH4 fluxes. Apart from RothC, 
all the SOM dynamics are able to simulate the most 
common agronomic management practices (i.e. tillage, 
fertilization, manuring, and crop rotation) (Table 2). 
More details on the representation of soil, plant 
ecophysiology, management and greenhouse gases 
and their weaknesses can be found in Brilli et al., 2017.
CIAT Research brief, December 2019 3
Table 1: Overview of the basic features of the surveyed models
DayCent RothC DNDC DSSAT CropSyst MONICA STICS Daisy APSIM
Scope Crop and 
SOC
modeling
SOC modeling Crop 
and SOC 
modeling
Crop 
and SOC 
modeling
Crop 
and SOC 
modeling
Crop 
and SOC 
modeling
Crop 
and SOC 
modeling
Crop 
and SOC 
modeling
Crop and 
SOC 
modeling
Timestep Daily Monthly Daily Daily Daily Daily Daily Hourly Daily
Extent of 
application
Point-scale, 
regional 
and global
Point-scale, 
regional and 
global
Point-scale 
and regional
Point-scale, 
regional 
and global
Point-
scale and 
regional
Point-scale 
and regional
Point-scale Point-scale Point-scale, 
regional 
and global
No. of SOC 
pools
3 5 4 3 3 3 2 3 3
Name of 
pools
Active, Slow, 
passive
Decomposable 
Plant Material 
(DPM), 
Resistant Plant 
Material (RPM), 
Microbial 
Biomass (BIO) 
and Humified 
Organic Matter 
(HUM)
Litter, 
microbes, 
Humads, 
Passive
Active, Slow, 
Passive
Active, 
Passive, 
Slow
Added 
Organic 
Matter 
(AOM), Soil 
Microbial 
Bio-mass 
(SMB), 
Native Soil 
Organic 
Matter 
(SOM)
Active and 
Inactive 
fractions
Added 
organic 
matter 
(AOM), Soil 
Microbial 
Biomass 
(SMB), and 
Native Soil 
Organic 
Matters 
(SOM)
Microbial 
biomass 
(BIOM), 
Humus 
(HUM), Inert 
organic 
matter (IOM)
Layered 
profile
Yes No Yes Yes Yes Yes Yes Yes Yes
Maximum 
no. of soil 
layers
14 1 Variable 
across soils
20 17 20 5 User 
specified
6
Maximum 
depth 
of SOC 
simulation
20 cm 30 cm User 
specified
All layers All layers 40 cm User 
specified
User 
specified
User 
specified
Equation 
governing 
decomposi-
tion
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
1st order 
kinetics
Soil water 
redistribu-
tion 
equation
Tipping 
bucket
Tipping bucket Tipping 
bucket
Tipping 
bucket
Richard’s 
equation
Tipping 
bucket
Tipping 
bucket
Richard’s 
equation
Tipping 
bucket and 
Richard’s 
equation
Water 
erosion 
module
No No Yes Yes Yes No Yes No Yes
Simulated 
gas fluxes
CO2 , N20, 
CH4 
CO2 CO2 , N20 CO2 , N20 CO2 , N20 CO2 , N20 CO2 , N20 CO2 , N20 CO2 , N20
Simulates 
soil 
nutrients
Nitrogen, 
Phosphorous, 
Sulphur
Carbon Nitrogen, 
Phosphorous,
Nitrogen, 
Phosphorous,
Nitrogen Nitrogen, 
Sulphur
Nitrogen Nitrogen Nitrogen 
and 
Phosphorous
Mulch 
layer 
affects 
water 
dynamics 
and heat 
balance in 
soil
Yes No Yes Yes Yes Yes Yes Yes Yes
CIAT Research brief, December 20194
Table 2: Overview of the crop management practices included the in surveyed models
Management DayCent RothC DNDC DSSAT CropSyst MONICA STICS DAISY APSIM
Crop harvest ✓ ✓ ✓ ✓ ✓ ✓ ✓
Tillage ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓
Fertilization ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓
Irrigation ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓
Manuring ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Crop rotation ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓
Intercropping ✗ ✗ ✓ ✓ ✗ ✗ ✓ ✓
Agroforestry ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗
Cover cropping ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓
Pesticides 
application
✗ ✗ ✗ ✗ ✗ ✗ ✗ ✓
Application of the models in sub-Saharan Africa
Table 3: A summary of the reviewed modeling studies in sub-Saharan Africa including the model, country/region of 
study, purpose of study and the reference. 
MODEL COUNTRY/
REGION
CROP PURPOSE OF STUDY REFERENCE
Century Sudan Millet and 
sorghum
Estimating SOC in different land use 
including cropland
Ardö and Olsson, 
2003
Century South Africa Sugarcane Validation, calibration and predicting 
SOC in sugarcane systems under 
different management
Galvados et al 
2009
Century and 
RothC
Nigeria and 
Sudan
Millet and 
groundnuts
Assessing the effects of improved 
agricultural management practices on 
SOC
Farage et al., 
2007
Century and 
RothC
Kenya Maize and 
maize-bean 
rotation
Model evaluation using long-term 
experiments 
Kamoni et al., 
2007
RothC Niger Millet Validation of SOC dynamics using long-
term experiments 
Nakamura et al., 
2011
DSSAT Ghana Maize and 
groundnuts
Economic analysis on management 
practices that enhance SOC 
sequestration
González-Estrada 
et al., 2008
Published modelling studies in sub-Saharan Africa were 
reviewed to assess the type of studies crops, purpose of 
the study and the applied models. The review indicates 
that APSIM and DSSAT are the most widely used crop-
soil mods in SSA with most of the studies being in West 
and Southern Africa region and only a few studies 
in East Africa (Table 3). Majority of the APSIM and 
DSSAT studies focussed mainly of crop production (i.e. 
yield) and a lot of emphasis has also laid on assessing 
and quantifying the impacts of climate change and 
management on yields. There are a few studies on SOC, 
but the key focus has been on model evaluation.  While 
agronomic management impacts on SOC are widely 
studied in other regions in the globe, there are only a 
few studies within SSA. Out of the 40 reviewed studies, 
majority were on point-scale modeling with only 2 being 
on a landscape level.
CIAT Research brief, December 2019 5
MODEL COUNTRY/
REGION
CROP PURPOSE OF STUDY REFERENCE
DSSAT Kenya and 
Uganda
Maize and 
maize-bean 
rotation
Validation of DSSAT using two long-
term experiments and projection of SOC 
changes
Musinguzi et al., 
2014
DSSAT Burkina Faso Cotton, 
sorghum, 
peanut and 
maize
Model evaluation with SOC and yields 
using long-term experiment data
Soler et al., 2011
DSSAT Cote d'Ivoire Maize Assessing the impacts of conservation 
agriculture on yields
Worou et al., 
2019
DSSAT Niger Maize Assessing the contribution of weather, 
crop and soil to uncertainties in 
simulated yields
Jones et al 2012
DSSAT Ghana Maize Calibrating and validating simulated 
grain and biomass yields in the  model
McCarthy et al 
2012
APSIM Malawi Maize and maize 
double legume 
intercrops
Model calibration, validation and 
assessing the sustainability of double-
legume rotations
Smith et al., 2016
APSIM Ghana Sorghum Modelling the impacts of nutrient and 
residue management on yield and SOC 
MacCarthy et al., 
2009
APSIM Eastern and 
Southern Africa 
- Ethiopia, 
Kenya, Tanzania, 
Malawi, 
Mozambique 
and Zimbambe
Maize Charactering maize production in 
different climate conditions
Seyoum et al., 
2017
APSIM Kenya Maize/
Agroforestry 
systems
Simulating the impacts of shading on 
maize
Dilla et al., 2018
APSIM Nigeria Maize Evaluation of APSIM using yield data 
from different maize cultivars
Yamusa and 
Akinseye, 2018
APSIM Ghana Maize Assessing the impacts of climate change 
and climate variability on maize yields
Fosu-Mensah et 
al., 2019
APSIM Malawi Maize and 
maize-pigeon 
pea
Model evaluation and assessing the 
impacts of climate change on yields
Ollengburger 
Mary 
2012iversity
APSIM Ethiopia Sorghum Assessing impacts of climate change and 
climate variability on production
Gebrekiros and 
Araya, 2015
APSIM Ghana Maize Assessing the effects of seasonal climate 
variability on efficiency of mineral 
fertilizer
MacCarthy et al., 
2015
APSIM Tanzania Maize Assessing the impacts of improved 
management practices on maize yields 
under current and future climate
Tumbo et al., 
2012
APSIM Eastern and 
Southern Africa 
- Ethiopia, 
Kenya, Tanzania, 
Malawi, 
Mozambique 
and Zimbambe
Sorghum Assessing the impacts of increased 
temperatures on sorghum yields
Turner and Rao, 
2013
CIAT Research brief, December 20196
MODEL COUNTRY/
REGION
CROP PURPOSE OF STUDY REFERENCE
APSIM West Africa Sorghum Assessing the impacts of climate change 
on yields
Sultan et al., 
2014
APSIM West Africa Sorghum Assessing the options for climate change 
adaptation
Guan et al., 2017
APSIM Southern Africa Sorghum Quantifying the response of maize yield 
to projected climate change and key 
management practices (i.e. planting 
date, cultivar, fertilizer use)
Rurinda et al., 
2015
APSIM Zimbabwe Maize Model calibration and simulating 
yield response to reduced tillage and 
mulching
Mupangwa et al., 
2011
APSIM Niger Millet Assessing impacts of nitrogen 
management on yields
Akponikpè et al., 
2010
APSIM South Africa Maize Assessing impacts of no-till on water 
fluxes and maize productivity
Mupangwa and 
Jewitt, 2011
APSIM Kenya Maize Evaluation of the model with data on 
nitrogen and residue man-agement
Kisaka et al., 
2016
APSIM South Africa Sorghum-
cowpea 
intercrop
Evaluation of growth, yield and crop 
water use
Chimonyo et al., 
2016
APSIM Zimbabwe Maize Model calibration for maize yield and N 
mineralization and simulat-ing the effects 
of tillage, fertilization management 
and planting dates on yields and N 
mineralization
Masvaya et al., 
2018
APSIM Zimbabwe Maize-Mucuna 
rotation
Comparing conventional farmer 
practices with improved practices 
comprising of manure application and 
rotations with cover crop (i.e. Mucuna)
Masikati et al., 
2014
APSIM Ghana Maize Simulating the long term influence of 
nitrogen and phosphorous on maize 
yield
Fosu-Mensah et 
al., 2012
APSIM Malawi Maize Assessing the effective use of nitrogen 
and phosphorous with rainfall variations
Kamanga et al., 
2014
DSSAT and 
APSIM
Southern 
Ethiopia
Maize Assessing maize growth and yield under 
present and future climate
Araya et al., 2015
DSSAT and  
APSIM
West Africa Sorghum Assessing the performance of the models 
in simulating sorghum germplasm in 
different climate and soil conditions
Akinseye et al 
2014
CropSyst Burkina Faso Millet Simulating yields across different 
climatic conditions
Badini et al., 
1997
CropSyst Cameroon Maize, sorghum, 
groundnut,
and soybean
Yield validation Tingem et al., 
2009
CropSyst Kenya Maize and 
Maize-Tephrosia 
rotation
Simulating nitrogen dynamics and 
nitrous oxide emissions in a long-
term trial under integrated soil fertility 
management
Sommer et al., 
2016
STICS Mali Sorghum Simulating crop developments Folliard et al., 
2004
CIAT Research brief, December 2019 7
Conclusion
The objective of this research brief was to summarize 
the main features of some of the most widely used crop 
models and assess their application in sub-Saharan 
Africa (SSA). The conducted survey indicates that most 
models can simulate crop growth and SOC dynamics 
with the exception of RothC, which simulates on SOC 
dynamics. Except for agroforestry and intercropping, 
the other common agronomic management practices 
are well represented in most of the models. Out of the 
9 surveyed models, APSIM and DSSAT are the most 
widely used in the region. For SOC, the emphasis has 
been on calibrating and validating the models with only 
a few study with model application in understanding 
the drivers of SOC dynamics and the impacts of 
agronomic management practices. Although this review 
may not be exhaustive, it shows that despite the use 
of models gaining momentum in SSA the focus has 
been on point-scale modelling. Furthermore, modelling 
studies in the East Africa region still remain scarce 
compared to West and Southern Africa. While model 
evaluation is critical in identifying their strength and 
weaknesses, an application of the model at wider scales 
would increase their application in informing policies 
within their region. This study shows that models 
are still underutilized especially for assessing and 
quantifying the drivers of crop and SOC dynamics at 
wider scales in SSA. 
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Contacts
Sylvia Sarah Nyawira
Post doctoral research scientist, 
Africa focal point ecosystem services and 
environmental impact research theme
s.nyawira@cgiar.org