# Evaluating DayCent and STICS in simulating the long-term impact of contrasting organic resource amendments on soil organic carbon and maize yields in sub-Saharan Africa

Antoine Couedel, Moritz Laub, Rindra Ranaivomanana, Gatien Falconnier, Rémi Cardinael, Monicah Wanjiku Mucheru-Muna, Daniel Mugendi, Bernard Vanlauwe, Johan Six, Marc Corbeels

► To cite this version:

Antoine Couedel, Moritz Laub, Rindra Ranaivomanana, Gatien Falconnier, Rémi Cardinael, et al. Evaluating DayCent and STICS in simulating the long-term impact of contrasting organic resource amendments on soil organic carbon and maize yields in sub-Saharan Africa. Field Crops Research, 2026, 335, pp.110169. 10.1016/j.fcr.2025.110169. hal-05318388

HAL Id: hal-05318388

https://hal.science/hal-05318388v1

Submitted on 16 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Evaluating DayCent and STICS in simulating the long-term impact of contrasting organic resource amendments on soil organic carbon and maize yields in sub-Saharan Africa

Antoine Couëdel<sup>a,b,* ,1</sup> ◆, Moritz Laub<sup>c,1</sup>, Rindra Ranaivomanana<sup>a,b</sup>,

Gatien N. Falconnier<sup>a,d,e,f</sup>, Rémi Cardinael<sup>a,d,f</sup>, Monicah Wanjiku Mucheru-Muna<sup>g</sup>,

Daniel Mugendi<sup>h</sup>, Bernard Vanlauwe<sup>i</sup>, Johan Six<sup>c</sup>, Marc Corbeels<sup>a,i,j</sup>

ARTICLE INFO

Keywords:

Soil organic matter

Long-term experiments

Ensemble modeling

Model intercomparison

Integrated Soil Fertility Management

Sustainable intensification

A B S T R A C T

**Problem:** Low crop yields in sub-Saharan Africa mainly result from low soil fertility and insufficient nutrient inputs. A key component of Integrated Soil Fertility Management (ISFM), namely combining inputs of mineral fertilizers and organic resources, presents an opportunity to boost yields and maintain soil organic carbon (SOC) stocks in the long run. Soil-crop models help to assess the performance of ISFM under contrasting soil, climate, and management combinations. Yet, to date, most soil-crop models have been calibrated and tested in temperate conditions.

**Objective:** Our objective was to evaluate and compare the performance of two different soil-crop models, DayCent and STICS, to represent crop yields and SOC dynamics under contrasting organic resource amendments.

**Methods:** We used a large dataset representing 3384 cropping situations (site x season x treatment) from four long-term experiments in Kenya. Each experiment included the same treatments with the addition of two quantities of low- to high-quality organic resource amendments (high vs low C/N ratio, respectively), with (+N) and without (-N) mineral nitrogen fertilizer. Each treatment included a cropped and uncropped subplot, allowing for a unique stepwise calibration of soil and crop parameters.

**Results:** Both models represented SOC and yield dynamics with similar accuracy across sites and treatments. They reproduced SOC dynamics well (nRMSE below 30 %) in the two clayey soils sites but not in the two sandy soils. Yet, in most sites they reproduced well SOC differences between high (Farmyard manure, *Thithonia* and *Calliandra*) and low-quality (maize stover and sawdust) organic resources. Models reproduced the average yield across sites and treatments similarly. They reproduced the positive effects of high-quality organic resources and the addition of mineral N on maize yield well. Models had similar inaccuracy in reproducing yield and yield variability under poor-quality organic resources and -N treatments.

**Conclusion:** The stepwise calibration approach used in this study enabled highlighting the models' strengths and weaknesses in soil and plant simulations. The results suggest that the two models have similar strengths and struggle with the same problems despite having different structures. Collecting detailed plant (leaf area index, plant N uptake) and soil (water, nitrogen dynamics) in-season data from long-term experiments will be critical to exploit the full model complexity and improve their accuracy for tropical conditions.

<footnote><sup>a</sup> AIDA, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>b</sup> CIRAD, UPR AIDA, Montpellier F-34398, France</footnote>

<footnote><sup>c</sup> Department of Environmental Systems Science, ETH Zurich, Switzerland</footnote>

<footnote><sup>d</sup> CIRAD, UPR AIDA, Harare, Zimbabwe</footnote>

<footnote><sup>e</sup> International Maize and Wheat Improvement Centre (CIMMYT)-Zimbabwe, Harare, Zimbabwe</footnote>

<footnote><sup>f</sup> Department of Plant Production Sciences and Technologies, University of Zimbabwe, Harare, Zimbabwe</footnote>

<footnote><sup>g</sup> Department of Environmental Sciences and Education, Kenyatta University, Nairobi 43844-00100, Kenya</footnote>

<footnote><sup>h</sup> Department of Water and Agricultural Resource Management, University of Embu, Embu 6-60100, Kenya</footnote>

<footnote><sup>i</sup> International Institute of Tropical Agriculture (IITA), Nairobi, Kenya</footnote>

<footnote><sup>j</sup> CIRAD, UPR AIDA, Nairobi, Kenya</footnote>

<footnote>* Corresponding author at: AIDA, Univ Montpellier, CIRAD, Montpellier, France.</footnote>

<footnote>E-mail address: antoine.couedel@cirad.fr (A. Couëdel).</footnote>

<footnote n="1">¹ contributed equally to this work</footnote>
# 1. Introduction

In many regions of sub-Saharan Africa (SSA), soil fertility decline in low-input systems is a major constraint to crop productivity on small-holder farms (Cobo et al., 2010; Falconnier et al., 2023; Sanchez, 2002). The yields of most staple crops are low in many SSA countries, and in contrast to all other continents, yields have not substantially increased in recent decades (FAOSTAT, 2023). Hence, yield gaps in SSA are historically high, largely due to soil nutrient limitations (ten Berge et al., 2019; Tittonell and Giller, 2013; Van Ittersum et al., 2016).

With the projected increase in temperatures and rainfall patterns (IPCC, 2023), both crop yields (through increased heat and drought stress, e.g. Rezaei et al., 2023) and soil organic matter (SOM) levels (through higher mineralization rates, e.g. Nissan et al. (2023) are at risk of decreasing even further, also leading to negative soil-crop feedback loops (Basso et al., 2018; Couëdel et al., 2024). Consequently, crop management practices that not only increase crop yields but also maintain and enhance SOM under current and future climate are gaining attention. In the context of SSA, integrated soil fertility management (ISFM), aiming at maximizing the agronomic use efficiency of applied nutrients through locally adapted applications of a combination of mineral fertilizer and organic resources, comprises a set of such practices to reduce maize yield gaps by alleviating nutrient limitations and sustaining soil fertility (Vanlauwe et al., 2010). Indeed, while mineral fertilizers are important for short-term macronutrient supply to crops, organic resource amendments are crucial for maintaining general soil health in the long term. However, organic resource amendments differ in their quality and decomposition rates, thereby influencing their effectiveness in soil fertility management. ISFM emphasizes, when available, the use of 'high quality' organic resource amendments, i.e. amendments with a low C/N and low lignin and polyphenol content, to ensure rapid residue decomposition without excessive immobilization of available mineral nitrogen (Palm et al., 2001).

Long-term experiments are key for assessing the conditions in which ISFM effectively sustains both soil fertility and crop yields (e.g. Cardinnael et al. 2022; Laub et al. 2023a, 2023b). Nevertheless, because of practical and financial limitations, experiments alone cannot fully capture (i) the vast diversity of climates and soils where crops are grown, and (ii) the wide range of cropping practices implemented by farmers (e. g. crop species, organic resource quality and quantity, etc.). To address these limitations, process-based soil-crop models offer a valuable tool for assessing and understanding the performance of cropping systems and the related soil and crop management practices. Understanding how model accuracy is influenced by the site conditions, as well as the type, quality, and quantity of organic resource amendments, is critical for determining their usefulness in exploratory studies on the role of ISFM in SSA. If calibrated and evaluated robustly, models can then facilitate extrapolation of results in space and time (Van Ittersum et al., 2016) and enable scenario analysis by accounting for interactions between genotype, environment (climate, soil) and management practices under both current and future climates (e.g. Falconnier et al., 2020).

While most soil-crop models have been well calibrated and tested in temperate conditions, several studies showed that their performance is lower in tropical conditions (Couëdel et al., 2024; Falconnier et al., 2020; Nyawira et al., 2021). As a consequence, global modelling studies often rely on model parameterizations that perform poorly in tropical regions, leading to inadequate estimates of potential yield productivity, soil fertility impacts and food security outcomes (e.g Rattalino Edreira et al., 2021). This uncertainty is exacerbated by the fact that model calibration in tropical conditions need to account for the fact that i) crops are exposed to more extreme environmental conditions (water and heat stresses), and ii) soil mineralogy and (thus) soil characteristics in the highly weathered tropical soils are profoundly different from temperate soils (Doetterl et al., 2015; Von Fromm et al., 2021). Furthermore, the efficacy of SOM formation may be different in tropical soils, as carbon use efficiency of organic resources is thought to decrease

at higher temperatures as a result of increased stress on microorganisms (Frey et al., 2013). Given these challenges, assessing model performance under tropical conditions and evaluating how key processes are represented is crucial to improving their reliability for modeling of ISFM practices in SSA regions.

STICS and DayCent are two widely used soil-crop models that are being increasingly tested in tropical conditions. However, they have so far been evaluated either in short-term experiments for their ability to reproduce the effects of mineral fertilization or agroecological practices (Falconnier et al., 2020; Sow et al., 2024; Traoreé et al., 2022), or in long-term trials examining the effect of a single residue type on soil organic carbon (SOC) (Couëdel et al., 2024), but never across contrasting organic amendments in tropical environments. Each model represents a specific set of hypotheses about key processes in the soil-plant-atmosphere continuum, and each hypothesis is subject to different variables that can trigger changes and lead to specific model outcomes. Beyond ensemble modeling using median or mean outcomes (Couëdel et al., 2024; Falconnier et al., 2020; Gaillard et al., 2018; Georgiou et al., 2021; Yin et al., 2017), comparing processes across models can reveal their strengths and limitations in representing specific processes and help in formulating recommendations for model improvements.

The aim of this study was to simultaneously quantify the ability of two current soil-crop models to reproduce changes in SOC and crop yield for contrasting combinations of organic resource amendments and mineral fertilizer (i.e., ISFM) in tropical conditions. To do so, we calibrated and evaluated STICS and DayCent for maize yields and SOC dynamics in four ISFM long-term experiments under contrasting pedoclimatic conditions in tropical Kenya. These four experiments applied the same treatments including organic resources of different qualities and quantities, either with or without mineral N fertilizer. This unique dataset, including uncropped and cropped treatments and representing altogether 3384 cropping situations (site x season x treatment), enabled a stepwise and robust calibration of both soil and crop components. Our hypothesizes were that i) both models are able to reproduce the different impact of high-quality versus low-quality organic resource amendments on SOC dynamics, ii) both models are more accurate in reproducing near-potential yield compared to yield under stressed (limited water and N) conditions, iii) DayCent, with its more detailed representation of SOC better simulate SOC dynamics, while STICS, with a more detailed representation of crop physiology, will better simulate crop yields.

# 2. Materials and methods

## 2.1. Experimental data

### 2.1.1. Sites and experimental design

The four long-term experiments used for model calibration and evaluation in this study are located in central Kenya (in Embu (-0.517°N, 37.459°E) and Machanga (-0.793°N, 37.664°E), initiated in 2002) and western Kenya (in Sidada (0.143°N, 34.422°E) and Aludeka (0.574°N, 34.191°E), initiated in 2005). They cover a range of pedoclimatic conditions. Temperatures are lowest in Embu, with a mean annual temperature of 20 °C and 1200 mm of annual rainfall. Machanga has a mean annual temperature of 24°C and, with 800 mm annually, receives the lowest amount of rainfall. The sites at Sidada (23°C, 1700 mm) and Aludeka (24°C, 1700 mm) have significantly more rainfall. All sites have two rainy seasons per year – maize is grown in both of them. The long rainy season starts in March, and the short rainy season in September/ October. Soils at Machanga and Aludeka have a low clay content and initial SOC concentration (13% clay and <1% SOC), while Sidada and Embu soils contain higher levels of clay and SOC (56 and 60% clay, and 1.5 and 3% SOC, respectively).

The four experiments were set up as a split-plot design with three replicates. Simulations were done using averaged data across all three replicates. The experiments had an identical setup of organic resource
treatments as the main plot, with the application or absence of mineral N fertilizer (120 kg N ha⁻¹ season⁻¹, i.e. 240 kg N ha⁻¹ yr⁻¹) being the split plot. A quarter of each split plot was left bare, maintained through regular manual weeding. Apart from the absence of crops, it was managed identically to the rest of the treatment plot. Organic resource additions varied in quality, i.e. prunings from *Tithonia diversifolia* (Hemsl.) A.Gray (TD) and *Calliandra calothyrus* Meisn. (CL) as high-quality organic amendments compared to *Zea mays* L. stover (MS) and sawdust from *Grevillea robusta* A.Cunn. ex R.Br. trees (SD) as low-quality organic amendments. Additionally, there was a treatment with locally available farmyard manure (FYM) and a control treatment (CT) without any organic resource addition. Here, low-quality organic resources were defined as those with high lignin and polyphenol content and high C/N, while high-quality organic resources had a low C/N (Palm et al., 2001). Organic resources were applied around January/February, only once a year. The two application rates were 1.2 and 4 C ha⁻¹ yr⁻¹, with organic amendments incorporated to about 15 cm soil depth using hand hoes, as part of the regular hand ploughing for seedbed preparation before each season. Mineral N fertilizer in the form of calcium ammonium nitrate was split-applied each season, 40 N kg ha⁻¹ at planting and 80 N kg ha⁻¹ about six weeks later. All treatments received a blanket application of 60 kg P ha⁻¹ as triple superphosphate and of 60 kg K ha⁻¹ as muriate of potash at planting once each season. Manual weeding was done between two and three times per season. Pesticides against armyworm, stemborer or termites were applied when necessary.

Further details on the experiments can be found in Chivenge et al. (2009); Gentile et al. (2009), (2011) and Laub et al. (2023a), (2023b).

## 2.1.2. Measurements

We used the combined data of all soil sampling campaigns that were conducted throughout the experiments (further details in Laub et al., 2023a), including the latest sampling in 2021 where both cropped and uncropped soils were sampled (see Table 2 and Laub et al., 2023a). Sampling intervals varied by site. Initial soil data from the establishment of each experiment were available for all four sites. In Embu and Machanga, regular soil sampling campaigns were conducted every two to three years since the first experimental year (2002), while in Sidada and Aludeka, regular soil sampling after the first experimental year (2005) was only initiated in 2018, due to budget constraints. Soil sampling of the uncropped plots was conducted in 2021, only (together with cropped plots). Maize grain yield and the aboveground biomass were measured in each cropping season between 2002/2005 and 2020 (further details in Laub et al., 2023b).

# 2.2. Model descriptions

## 2.2.1. STICS model

The STICS soil-crop model (Brisson et al., 2009) simulates the dynamics of agricultural and environmental variables (e.g. crop yield, above- and belowground biomass, soil moisture and mineral N content, N leaching and SOC, greenhouse gases) over cropping seasons on a daily time step, considering the impact of weather, soil, and crop management practices (e.g. mineral and organic fertilization, tillage and crop residue management). It was designed as a generic model that can be easily adapted to different crops and environmental conditions. The simulated net primary productivity of a cropping system depends on solar radiation, temperature and water or nutrient (N only here) stresses. Crops are defined by plant parameters, with a set of eco-physiological options. Plant parameters include both crop- and cultivar-specific parameters. Crop development is driven by cultivar-specific cumulative thermal time, with dynamic daily simulation of root growth, leaf area index, soil moisture content, aboveground biomass, plant N uptake, and grain yield. Water and nitrogen stress factors are computed daily, based on ratios of actual to potential evapotranspiration and nitrogen supply to demand, respectively. These stress factors (ranging from 0 to 1) influence key processes like radiation use efficiency, LAI, and transpiration

(Beaudoin et al., 2023).

In the STICS version that was used (v.9.2), soil biological activity, and thus SOM decomposition is assumed to occur only in the topsoil (0–20 cm). SOM is divided into four pools: organic resources (external organic inputs and crop residues), microbial biomass, an active- and a stable SOM pool (Fig. 1). The decomposition of the first three pools follows first-order kinetics, while stable SOM does not decompose. Decomposition rates of all pools are influenced by soil conditions (temperature, water content and available soil nitrogen). The decomposition rates of organic resources depend on their C:N and result in microbial assimilation and CO₂ emissions depending on a microbial yield efficiency Yres. In turn, the microbial biomass is either mineralized to CO₂ or incorporated into the active SOM pool with a yield efficiency H (depending on the C:N of the organic resources). The potential decomposition rate of the active SOM pool is modulated by the environmental conditions (soil water content and temperature) and soil characteristics (clay and calcium carbonate contents, C:N ratio and pH). The stable SOC pool is not subject to decomposition.

## 2.2.2. DayCent model

DayCent is a crop-soil model that simulates C and N fluxes between the atmosphere, plants and soil on a daily time step (Del Grosso et al., 2001). To match the actual harvest dates, we used the DayCent mode without the use of growing degree days for crop development (DayCent version DD_EVI, 2020). DayCent simulates plant growth, the decomposition of organic resources and SOM, and thereby the mineralization of N. It applies rate modifiers to connect these processes to soil water content and temperature and can simulate the production of greenhouse gases. The simulated net primary productivity of a cropping system depends on solar radiation, temperature, and water or nutrient stresses (here we simulated only N), a concept originating from the CENTURY model (Parton et al., 1987). Crop phenology is not explicitly simulated but below a certain biomass value (called the seedling stage), a scalar, which is a linear function of biomass, reduces photosynthesis. The pools that contain SOC/N are conceptualized into the active, slow and passive SOM pools (Fig. 1). Despite having the same names, the active SOM pool in DayCent is more similar to the microbial pool in STICS, while the slow SOM pool in DayCent is more similar to the active SOM pool in STICS. In contrast to STICS, all DayCent pools are subject to first-order kinetics, with each pool having its own parameter for turnover time and a carbon use efficiency-related parameter (the fraction of C that is lost as CO₂ during decomposition, mathematically this is one minus the carbon use efficiency of STICS). For the active SOM, the amount of C that is lost as CO₂ during decomposition further depends on the amount of clay in the soil. In DayCent, the fractions of CO₂ lost during decomposition are a general property of each litter pool (in contrast to STICS where this is calibrated for each type of organic resource). The division between the two litter pools is determined by the measured lignin/N ratio of each organic resource (Parton et al., 1987).

# 2.3. Model calibration and evaluation procedure

In general, our parameterizations relied mostly on published sources and default values. Other parameters were obtained from measurements (i.e., C/N ratios of organic resources). The parameters whose values are more uncertain, such as the cultivar-specific crop parameters, the efficiency of carbon stabilization, and the initial SOC distributions, were adjusted to obtain good fits to the data. This model calibration consisted of a three-step approach including the parametrization of i) SOM turnover, ii) microbial carbon use efficiency of organic resources (those of SOM were kept at their standard values) and iii) maize growth (Fig. 1). For each step we identified the most influential parameters governing the behavior of each of the two models, thereby ensuring harmonized calibration across both models (Table 1). Parametrization was done manually, adjusting parameters to achieve the best fit between simulated and observed data.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** STICS and DAYCENT summarized formalisms used for each calibration step. CUE: Carbon Use Efficiency. OR: Organic Resource. LAI: Leaf Area Index. RUE: Radiation Use Efficiency.</figcaption></figure>

The first calibration step utilized SOC stock data from uncropped treatments (+/- N) only, to simulate the SOM turnover without organic resource and maize root inputs (Table 2). The ratio of the active/stable SOM pool in STICS and the corresponding ratio of the slow/passive SOM pool in DayCent were calibrated for each experimental site separately (Table 1). The rapidly cycling active pool SOM of DayCent was assumed to be 3% of total SOM, which is in the middle of the range specified in the manual (2–4%). The initial C/N ratio of the SOM pools in DayCent was chosen in a way that initial N values matched measured data and maintained the differences in C/N ratios between the pools, as given in the default parameterization (i.e., the slow pool had about double the C/N of the passive pool, while the active pool was in between those two). In STICS, active and stable SOM pools have the same C:N ratio set to the observed values at the beginning of the experiments.

The second calibration step utilized SOC stock data from the uncropped soils treatments with additions of organic resources of 4 C ha⁻¹ yr⁻¹ (+/- N), to calibrate the carbon use efficiency-related parameters for organic resources without the influence of maize roots (Table 2). In STICS, the parameter is called microbial yield efficiency (Yres), effectively representing microbial carbon use efficiency during organic resource decomposition. It was calibrated for each organic resource but remained constant across sites. In DayCent, there is no explicit carbon use efficiency parameter as in STICS; instead, the model defines parameters for the fraction of carbon lost at each decomposition step (which is effectively 1 – carbon use efficiency). The corresponding fractions of CO₂ loss during the decomposition of the metabolic and structural litter pool, including lignin (PMCO₂, PS1CO₂(1), PS1CO₂(2), and rsplig parameters) and the parameter determining the effect of clay on the stabilization of new SOM (PS1S3(2) parameter) were calibrated.

The plant calibration (step 3) utilized all available data from the cropped treatments, both without inputs and with 4 C ha⁻¹ yr⁻¹ of

organic resources (+/- N) (Table 2). Plant calibration in STICS included three sub-steps with three types of target variables, i) maize phenology, ii) total maize aboveground biomass at maturity and iii) grain yield. DayCent does not simulate the phenological stages of plants and therefore, the first sub-step was omitted. In STICS, the phenology calibration consisted of parameterizing the thermal constants associated with the maize development stages. The model was forced to go through the observed dates of flowering and maturity with manual calibration (stlevdrp and stdrpmat parameters; Table 1), in order to compute the temperature sums corresponding to the intervals between the maize development stages. We assumed that flowering occurred 165-degree days before the start of grain filling. The aboveground biomass calibration consisted of adjusting the parameters related to maize N uptake. Affinity constants of N uptake by roots (Kmabs1 and Kmabs2) and the maximum specific N uptake rate (Vmax1 and Vmax2) were adjusted to minimize the difference between simulated and observed aboveground biomass. Finally, the slope of the relationship between grain number and growth rate (cgrain) and the rate of increase of the harvest index vs. time (viticarbT) were adjusted to minimize the difference between simulated and observed grain yield. Other plant parameters were default values for maize in STICS.

In DayCent, the aboveground biomass calibration was conducted in a slightly different way, due to the difference in model structure, and based on the recommendations from the DayCent manual (Hartman et al., 2020). It consisted of adjusting the optimal (PPDF(1)) and maximum temperatures (PPDF(2)) for crop growth at the maize cultivar level (different in Embu, compared to others) and the amount of C going to roots under optimal conditions (FRTC(1), and FRTC(2)), as well as the increase of C going to roots under water- and N-stressed conditions (FRTC(3), and FRTC(4)). Finally, the N uptake efficiency (FAVAIL(1)) as well as the maximum C/N ratio of maize biomass (PRAMX(1,1), and
<table><caption>**Table 1**

Description of the model parameters used in the three calibration steps. SOM: soil organic matter, CL= *Calliandra calothyrsus*, FYM= farmyard manure, MS= maize stover, SD= sawdust, TD= *Tithonia diversifolia*.</caption><thead><tr><th colspan="4">STICS</th><th colspan="4">DayCent</th></tr><tr><th>Parameter</th><th>Description</th><th>Range (min-default-max)</th><th>Calibrated</th><th>Parameter</th><th>Description</th><th>Range (min-default-max)</th><th>Calibrated</th></tr></thead><tbody><tr><td>Step 1</td><td>FINERT_AL</td><td>Initial fraction of stable SOM pool for Aludeka</td><td>0.3-0.65-0.8</td><td>0.57</td><td>a/s/p ratio_AL</td><td>Ratio of C in the active/slow/passive SOM pool for Aludeka</td><td>Usually determined by spin-up and site history simulations</td><td>0.03/0.37/0.6</td></tr><tr><td></td><td>FINERT_EM</td><td>Initial fraction of stable SOM pool for Embu</td><td>0.3-0.65-0.8</td><td>0.15</td><td>a/s/p ratio_EM</td><td>Ratio of C in the active/slow/passive SOM pool for Embu</td><td>Usually determined by spin-up and site history simulations</td><td>0.03/0.37/0.6</td></tr><tr><td></td><td>FINERT_MA</td><td>Initial fraction of stable SOM pool for Machanga</td><td>0.3-0.65-0.8</td><td>0.20</td><td>a/s/p ratio_MA</td><td>Ratio of C in the active/slow/passive SOM pool for Machanga</td><td>Usually determined by spin-up and site history simulations</td><td>0.03/0.44/0.53</td></tr><tr><td></td><td>FINERT_SI</td><td>Initial fraction of stable SOM pool for Sidada</td><td>0.3-0.65-0.8</td><td>0.35</td><td>a/s/p ratio_SI</td><td>Ratio of C in the active/slow/passive SOM pool for Sidada</td><td>Usually determined by spin-up and site history simulations</td><td>0.03/0.43/0.54</td></tr><tr><td>Step 2</td><td>yres_FYM</td><td>Carbon assimilation yield by the microbial biomass during FYM decomposition</td><td>0.1-0.41-0.5</td><td>0.25</td><td>PMCO2</td><td>Fraction of C lost as CO<sub>2</sub> during litter decomposition</td><td>0.8-0.55-0.4</td><td>0.65</td></tr><tr><td></td><td>yres_CL</td><td>Carbon assimilation yield by the microbial biomass during CL decomposition</td><td>0.1-0.41-0.5</td><td>0.20</td><td>PS1CO2(1), PS1CO2(2), and rsplig</td><td>Fraction of C lost as CO<sub>2</sub> during structural litter and lignin decomposition</td><td>0.9-0.8-0.55</td><td>0.90</td></tr><tr><td></td><td>yres_TD</td><td>Carbon assimilation yield by the microbial biomass during TD decomposition</td><td>0.1-0.41-0.5</td><td>0.20</td><td>PS1S3(2)</td><td>Fraction of C going from active to passive SOM pool</td><td>0.02-0.032-0.06</td><td>0.06</td></tr><tr><td></td><td>yres_SD</td><td>Carbon assimilation yield by the microbial biomass during SD decomposition</td><td>0.1-0.41-0.5</td><td>0.25</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>yres_MS</td><td>Carbon assimilation yield by the microbial biomass during MS decomposition</td><td>0.1-0.41-0.5</td><td>0.35</td><td></td><td></td><td></td><td></td></tr><tr><td>Step 3.1</td><td>stlevdrp</td><td>Cumulative thermal time between emergence and beginning of grain filling</td><td>1-995-6000</td><td>1200 (cultivars DH04, Kutamani, WS502, WS403), 1000 (cultivar H513), 1150 (cultivar IR)</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>stdrmat</td><td>Cumulative thermal time between the beginning of grain filling and maturity</td><td>0-640-2000</td><td>900 (all cultivars but IR), 1000 (cultivar IR)</td><td></td><td></td><td></td><td></td></tr><tr><td>Step 3.2</td><td>Kmabs1</td><td>Affinity constant of N uptake by roots for the fast uptake system</td><td>20-50-200</td><td>50 (all cultivars but H513), 40 (cultivar H513)</td><td>PPDF(1)</td><td>Optimum temperature for growth of maize for all sites, but Embu</td><td>10-30-40°C</td><td>30.25</td></tr><tr><td></td><td>Kmabs2</td><td>Affinity constant of N uptake by roots for the low uptake system</td><td>4000-25000-40000</td><td>25000 (all cultivars but H513), 20000 (cultivar H513)</td><td>PPDF(1)_EM</td><td>Optimum temperature for growth of maize for Embu</td><td>10-30-40°C</td><td>28</td></tr><tr><td></td><td>Vmax1</td><td>Maximum specific N uptake rate with the low affinity transport system</td><td>0.0002-0.0018-0.01</td><td>0.002 (all cultivars but H513), 0.0045 (cultivar H513)</td><td>PPDF(2)</td><td>Maximum temperature for growth of maize for all sites but Embu</td><td>20-45-50°C</td><td>46.5</td></tr><tr><td></td><td>Vmax2</td><td>Maximum specific N uptake rate with the high affinity transport system</td><td>0.002-0.017-0.1</td><td>0.025 (all cultivars but H513), 0.050 (cultivar H513)</td><td>PPDF(2)_EM</td><td>Maximum temperature for growth of maize for Embu</td><td>20-45-50°C</td><td>45</td></tr><tr><td></td><td>Croirac</td><td>Elongation rate of the root apex</td><td>0-0.15-0.5</td><td>0.07 (all cultivars but H513), 0.09 (cultivar H513)</td><td>FAVAIL(1)</td><td>Fraction of N available to plants per day</td><td>0-0.15-1</td><td>0.2</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>FRTC(1)</td><td>Amount of C going to roots at no stress</td><td>0-0.5-1</td><td>0.37</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>FRTC(2)</td><td>(1/2) and maximum increase under stress (4/5) for all sites but Embu</td><td>0-0.2-1</td><td>0.15</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>FRTC(4)</td><td></td><td>0-0.1-1</td><td>0.3</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>FRTC(5)</td><td></td><td>0-0.1-1</td><td>0.15</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 1 (continued)</caption><thead><tr><th colspan="3">STICS</th><th rowspan="2">Range (min-default-max)</th><th rowspan="2">Calibrated</th><th colspan="3">DayCent</th></tr><tr><th>Parameter</th><th>Description</th><th>Parameter</th><th>Description</th><th>Range (min-default-max)</th><th>Calibrated</th></tr></thead><tbody><tr><td rowspan="12">Step 3.3</td><td rowspan="12">cgrain vitircarbT</td><td rowspan="12">Slope of the relationship between grain number and growth rate Rate of increase of the C harvest index vs thermal time</td><td rowspan="12">0.01-0.05-1</td><td rowspan="12">0.030 (cultivars DH04, H513, IR), 0.050 (cultivar WS502), 0.070 (cultivar Kutamani) 0.0006 (cultivars DH04, IR), 0.0004 (cultivar H513), 0.001 (cultivars Kutamani, WS502), 0.0007 (cultivar WS403)</td><td>PRAMX(1,1) PRAMX(1,2)</td><td>Maximum aboveground C/N ratio at planting and full biomass for all sites but Embu</td><td>1-40-200 1-125-400</td><td>44 135</td></tr><tr><td>FRTC(1)E</td><td>Amount of C going to roots at no stress (1/2) and maximum increase under stress (4/5) for Embu</td><td>0-0.5-1 0-0.2-1 0-0.1-1 0-0.1-1</td><td>0.48 0.15 0.27 0.2</td></tr><tr><td>FRTC(2)E</td><td>FRTC(4)E</td><td>FRTC(5)E</td><td>Maximum aboveground C/N ratio at planting and full biomass for Embu</td><td>1-40-200 1-125-400</td><td>30 125</td></tr><tr><td>PRAMX(1,1)E</td><td>PRAMX(1,2)E</td><td>Maximum harvest index for all sites but Embu</td><td>0-0.5-1</td><td>0.44</td></tr><tr><td>HIMAX</td><td>HIMAX_E</td><td>Maximum harvest index for Embu</td><td>0-0.5-1</td><td>0.34</td></tr><tr><td rowspan="4">5.0E-5-0.0011-0.002</td><td rowspan="4">HIMAX_E</td><td rowspan="4">Strength of water stress on Harvest Index for all sites but Embu Number of months prior harvest that accumulate water stress for all site but Embu</td><td rowspan="4">0-0.5-1 1-1-12</td><td rowspan="4">0.41 1.5</td></tr><tr><td>HIWSF</td><td>Strength of water stress on Harvest Index for Embu</td><td>0-0.5-1</td><td>0.41</td></tr><tr><td>HIMON(1)</td><td>Number of months prior harvest that accumulate water stress for all site but Embu</td><td>1-1-12</td><td>1.5</td></tr><tr><td>HIWSE_E</td><td>Number of months prior harvest that accumulate water stress for Embu</td><td>0-0.5-1 1-1-12</td><td>0.3 1.0</td></tr></tbody></table>

PRAMX(1,2)) were adjusted. For the calibration of grain yield, the maximum harvest index (HIMAX) and the influence of water (HIWSD) stress on the harvest index were altered together with adjusting the period for calculating the water stress effect on harvest index (HIMON (1)). All other parameter values were maintained at the level of the default maize parameterization of DayCent with the highest productivity (called “C6” in the crop.100 file).

Model evaluation was carried out on cropped treatments with organic resources application of 1.2 C ha⁻¹ yr⁻¹ (+N/-N; Table 2). Model performance was evaluated for calibration and evaluation datasets separately and quantified by calculating the normalised root mean square error (nRMSE), the model efficiency (EF), and the mean squared error (MSE) decomposed into squared bias (SB), lack of correlation (LC) and non-unity slope (NU), as follows (Loague and Green, 1991):

$$MSE_y = \frac{1}{n} \sum_{z=1}^{n} (O_{yz} - S_{yz})^2 \quad (1)$$

$$RMSE_y = \sqrt{MSE_y} \quad (2)$$

$$nRMSE_y = \frac{RMSE_y}{O_y} \times 100 \quad (3)$$

$$EF_y = 1 - \frac{\sum_{z=1}^{n} (O_{yz} - \bar{O}_y)^2 - \sum_{z=1}^{n} (O_{yz} - S_{yz})^2}{\sum_{z=1}^{n} (O_{yz} - \bar{O}_y)^2} \quad (4)$$

where MSE_y is the mean-squared-error, RMSE its root and nRMSE the RMSE value normalized by the mean. EF_y is the Nash-Sutcliffe modeling efficiency. O_yz and S_yz are respectively the observed and simulated value of the zth measurement of the yth measurement type, O_y the observed mean of the yth measurement type.

MSE_y was further partitioned into squared bias (SB), non-unity slope (NU) and lack of correlation (LC), as suggested by Gauch et al. (2003). They are expressed as a percentage of the MSE_y:

$$SB_y(\%) = \frac{(\bar{O}_y - \bar{S}_y)^2}{MSE_y} \times 100 \quad (5)$$

$$NU_y(\%) = \frac{(1 - b_y)^2}{MSE_y} \times \frac{\sum_{z=1}^{n} O_{yz}^2}{n} \times 100 \quad (6)$$

$$LC_y(\%) = \frac{(1 - r_y)^2}{MSE_y} \times \frac{\sum_{z=1}^{n} S_{yz}^2}{n} \times 100 \quad (7)$$

where, S_y is the simulated mean of the yth measurement type, b is the slope of the regression of S on O and r is the correlation coefficient between O and S. The indicators LC, SB and NU indicate the model errors type, i.e, a high LC indicates that it is mostly random error, a high SB a systematic bias, while a high NU indicates model sensitivity problems.

## 3. Results

### 3.1. Model calibrations

#### 3.1.1. Uncropped soil calibration (steps 1 and 2)

In the first calibration step dealing with SOM turnover (Fig. 1), both models were capable of effectively reproducing total SOC loss at the end of the experiments (i.e. after 20 years) in uncropped soils (Figure S1). This was despite STICS having a huge range of the SOM fraction being in the inert pool (from 0.15 in Embu to 0.57 in Aludeka), while DayCent had a much narrower range of SOM being in the passive pool (from 0.53 in Machanga, to 0.6 in Aludeka and Embu) (Table 1). DayCent slightly outperformed STICS, with nRMSE values 2 % and 9 %, respectively. In the second calibration step, which addressed carbon use efficiency-related parameters for uncropped soils receiving organic resources (4 t
<caption>**Table 2**</caption>

Experimental treatments used for model calibration (3 steps) and model evaluation. The long-term experiments were conducted in four sites in Kenya with five types of added organic resources (*Calliandra calothyrsus*, farmyard manure, maize stover, sawdust, *Tithonia diversifolia*).

<table><thead><tr><th colspan="2">Management</th><th rowspan="2">N fertilisation (kg N ha<sup>-1</sup> year<sup>-1</sup>)</th><th colspan="3">Calibration</th><th rowspan="2">Evaluation</th></tr><tr><th>Soil cover</th><th>Organic resource amount (t C ha<sup>-1</sup> year<sup>-1</sup>)</th><th>Step I</th><th>Step II</th><th>Step III</th></tr></thead><tbody><tr><td rowspan="4">Uncropped</td><td>0</td><td>0</td><td>x</td><td></td><td></td><td></td></tr><tr><td>0</td><td>240</td><td>x</td><td></td><td></td><td></td></tr><tr><td>4</td><td>0</td><td></td><td>x</td><td></td><td></td></tr><tr><td>4</td><td>240</td><td></td><td>x</td><td></td><td></td></tr><tr><td rowspan="5">Cropped</td><td>0</td><td>0</td><td></td><td></td><td>x</td><td></td></tr><tr><td>0</td><td>240</td><td></td><td></td><td>x</td><td></td></tr><tr><td>1.2</td><td>0</td><td></td><td></td><td></td><td>x</td></tr><tr><td>1.2</td><td>240</td><td></td><td></td><td></td><td>x</td></tr><tr><td>4</td><td>0</td><td></td><td></td><td>x</td><td></td></tr><tr><td></td><td>4</td><td>240</td><td></td><td></td><td>x</td><td></td></tr></tbody></table>

$ha^{-1} yr^{-1} +/\-N)$, the ranges of calibrated carbon use efficiencies were similar in STICS (0.2–0.35) and DayCent (0.1–0.3; computed from efficiencies per metabolic and structural pool and the split between the two). Both models could accurately capture the magnitude of SOC loss in the clayey sites (Embu and Sidada) (Figure S2.A). They were also capable of simulating reasonably well the effects of high-quality organic resources (FYM, CL, and TD) on SOC, particularly in Embu. However, both models showed lower accuracy in simulating SOC changes in the sandy soil of Aludeka, though they correctly simulated the sawdust treatment as having the highest SOC losses (about 30 % of initial SOC). Both models underestimated SOC losses in Machanga (sandy soil), likely due to soil erosion at this site, a process not accounted for in the models. Yet, at that site, STICS accurately represented the relative impact of all organic resource treatments on SOC change, while DayCent effectively simulated the relative difference between high-quality organic resource and sawdust (Figure S2).

### 3.1.2. Crop calibration (step 3)

In the third calibration step, dealing with crop development and growth, STICS was able to accurately reproduce maize phenology (Figure S3). Both models simulated the maize aboveground biomass and grain yield averaged across years for the different experimental treatments with mixed success. The nRMSE values ranged from 17 % to 38 % for aboveground biomass and 14–41 % for grain yield, and models could reproduce the observed variability with $R^2$ values ranging from 0.46 to 0.87 for aboveground biomass and 0.4–0.91 for yield (Figure S4.A and S5.A). Both models had a very good accuracy in simulating average yield and aboveground biomass in Aludeka, with nRMSE values ranging from 14 % to 20 % and EF from 0.7 to 0.9. In contrast, both models showed low accuracy for Sidada with negative EF for aboveground biomass values, showing bias in the simulations. In Embu, the average yield was not well simulated by DayCent (EF around 0, nRMSE of 24 %) but was acceptably captured by STICS (EF of 0.30, nRMSE of 20 %). In contrast, in Sidada STICS performed poorly (EF of −0.27, nRMSE of 27 %) while DayCent provided more acceptable simulations (EF of 0.46, nRMSE of 18 %). In general, both models tended to underestimate crop yield in Sidada and Embu, particularly when MS and SD were applied without mineral N (Figure S4.A and S5.A). Simulations in Machanga showed the lowest accuracy with DayCent (nRMSE of 38 % for aboveground biomass and 41 % for yield, EF of −0.12 aboveground biomass and 0.2 for yield), primarily due to an overestimation of aboveground biomass and yield when organic resources were applied (Figure S4.A and S5.A). These overestimations might be partly attributed to the model simulations underestimating SOC loss in Machanga.

Overall, despite some biases, both models effectively captured the differences between the effects of mineral fertilization (+N vs -N) and high-quality versus low-quality organic resources without nitrogen (-N) on average aboveground biomass and grain yield across all sites. When N was added, the models successfully represented differences in

aboveground biomass and yield between organic resources, except for DayCent in Embu and both models in Machanga (Figure S4.A and S5.A), where the underestimation of SOC loss in the organic resource treatments may have influenced crop growth simulations.

When examining yield variability across years at each site, both models underestimated the variability in -N treatments for the control (no organic input) and low-quality organic resources (MS and TD) (Figure S4.B and S5.B). For high-quality organic resources (FYM, TD, and CL) without N, the models captured yield variability more accurately, although STICS overestimated yield variability in Sidada and Embu (Figure S4.B and S5.B). With the addition of N, both models were able to effectively reproduce yield variability for all types of organic resources, except for DayCent in Embu, STICS in Aludeka and both models in Machanga.

## 3.2. Model evaluations

### 3.2.1. Soil organic carbon

Unlike for the calibration phase, the evaluation of the soil models was conducted using only data from cropped soils. Both models reproduced SOC changes for control and organic resource amendment treatments (1.2 t $ha^{-1} yr^{-1} +/\-N$) with similarly low accuracy and efficiency, with Sidada being the only site where models achieved positive EF values (0.34 for DayCent and 0.07 for STICS, Fig. 2). At the three other sites, the models showed bias, with a tendency to underestimate SOC loss, the exception being STICS in Aludeka where SOC losses were overestimated. On the other hand, both models effectively captured the magnitude of SOC loss in Sidada and, to some extent, in Embu, the two most clayey sites. Additionally, they mostly represented the differences between organic resources at both clayey sites, although in Sidada the simulated differences between organic resources were smaller than the observed values. In the sandy soil sites (Aludeka and Machanga), the models did not accurately reproduce the order of magnitude of SOC loss. They underestimated SOC loss in Machanga, while in Aludeka STICS overestimated it and DayCent underestimated it. Yet, both models successfully captured the differences between high- and low-quality organic resources in Machanga and Aludeka, although STICS showed lower accuracy in Aludeka. Observed differences in SOC stocks between the +N and the -N treatments were not significant (cf. Laub et al., 2023a), and this was also reflected in the model simulations (Fig. 2.B).

### 3.2.2. Crop aboveground biomass and grain yield

Both models similarly reproduced aboveground biomass and grain yield averaged across years at all sites, with nRMSE values ranging from 18 % to 47 % for aboveground biomass (EF from −1.63–0.76) and 13–63 % for yield (EF from −2.7–0.91; Fig. 3.A, 4.A). Both models accurately represented the higher yields in the +N treatment at all sites except Embu, where they simulated a positive effect of mineral N addition that was not supported by the data. The models also captured
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Model evaluations of DayCent and STICS. Observed versus simulated soil organic carbon (SOC) change (%) (0–20 cm soil depth) averaged across +/- N treatments at the end of the long-term experiments (A). Observed (dots) and simulated (lines) SOC change (%) (0–20 cm soil depth) for the control and organic resource +/- N treatments (B). Data are from four sites in Kenya under five different types of organic resources at 1.2 C ha⁻¹ yr⁻¹ +/- N (240 kg N ha⁻¹ yr⁻¹). Control= no organic resource applied, CL= *Calliandra calothyrsus*, FYM= farmyard manure, MS= maize stover, SD= sawdust, TD= *Tithonia diversifolia*.</figcaption></figure>

the effect of organic resources (in both -N and +N treatments), correctly showing that high-quality organic resources (FYM, CL, and TD) resulted in greater aboveground biomass and yield compared to low-quality organic resources (SD and MS). Although both models tended to

underestimate aboveground biomass and yield in the -N treatments at Sidada for all treatments and Embu for the low-quality and control treatments, they successfully reproduced the positive effects of high-quality organic resources.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Model evaluations of DayCent and STICS. Simulated versus observed maize aboveground biomass under five different types of organic inputs at 1.2 C ha⁻¹ yr⁻¹, +/- (240 kg N ha⁻¹ yr⁻¹). Average value across years (A) and interannual variability among years (B). The shaded grey area around the regression line represents the 95 % confidence interval of the fitted linear regression model. Control= no organic resource applied, CL= *Calliandra calothyrsus*, FYM= farmyard manure, MS= maize stover, SD= sawdust, TD= *Tithonia diversifolia*.</figcaption></figure>

At the site level, simulations in Aludeka exhibited the highest accuracy, with nRMSE values of 19 % and 13 % for yield (EF of 0.80 and 0.91) and 24 % and 18 % for aboveground biomass (EF of 0.58 and 0.76) for DayCent and STICS respectively. Simulations in Embu and Sidada (clayey soils) had lower accuracy, largely due to underestimation of

aboveground biomass and grain yield in both models, particularly in the -N treatments (Fig. 3.A and 4.A). Grain yield in the +N treatments in Embu was also overestimated. In contrast, simulations in Machanga had the lowest accuracy, with overestimation of aboveground biomass and grain yield with DayCent (in the +N treatments) and with STICS (for
high-quality organic resources in the -N treatments).

When examining yield variability across years at each site, both models underestimated yield variability in the -N treatments for the control and low-quality organic resources (MS and TD) (Fig. 3.B and 4. B). For high-quality organic resources without N, the models reproduced yield variability slightly better, although STICS overestimated it in Sidada and Embu, while DayCent underestimated it in Machanga and Sidada (Fig. 3.B and 4.B). With the addition of N, both models

successfully reproduced yield variability for all types of organic resources, except DayCent in Embu and Machanga.

### 3.3. Yield trends over the duration of the experiments

Observed maize yield trends differed across sites, with yield increases for most treatments at the sites with more rainfall in Western Kenya (Aludeka and Sidada) and yield decreases at the drier sites in

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Model evaluations of DayCent and STICS. Simulated versus observed maize grain yield under five different types of organic inputs at 1.2 C ha⁻¹ yr⁻¹, +/-N (240 kg N ha⁻¹ yr⁻¹). Average value across years (A) and interannual variability among years (B). The shaded grey area around the regression line represents the 95 % confidence interval of the fitted linear regression model. Control= no organic resource applied, CL= *Calliandra calothyrsus*, FYM= farmyard manure, MS= maize stover, SD= sawdust, TD= *Tithonia diversifolia*.</figcaption></figure>
Central Kenya (Embu and Machanga) (Fig. 5). Overall, models were not able to reproduce the magnitude of yield change at any site but were able to reproduce the direction of change (positive, neutral or negative change) for some site x treatment combinations. In Sidada, observed yield increase was reproduced by STICS but only for the +N treatments, while both models reproduced yield loss in the control and low-quality organic resource treatments. Yield increases for other treatments were also reproduced by STICS for high-quality organic resources, but only with the addition of N. For Embu, DayCent underestimated the yield decrease for high-quality organic resources without N, while both models reproduced the reduction for low-quality organic resources. Both models, however, simulated a yield increase with +N in Embu, despite observed yield decreases. For Machanga, only STICS reproduced yield loss and only under the -N treatments.

In summary, both models reproduced some trends in yield change such as the yield decrease in Embu and Machanga for -N but not for +N treatments. STICS reproduced yield increase in Aludeka and Sidada in the +N treatments but not in the -N treatments for high-quality organic resources, while DayCent only reproduced the yield losses at Embu and Sidada with low-quality organic resources.

# 4. Discussion

## 4.1. Value of models to assess the impact of ISFM with different organic resource quality

To our knowledge, this is the first study in SSA to comprehensively evaluate the performance of two widely used models (STICS and Day-Cent) in simulating maize grain yield and SOC responses to applications of mineral N fertilizer and organic resources of different quality in the context of ISFM. Both models simulated reasonably well the effects of mineral N fertilizer and differentiated high- vs. low-quality organic resources on grain yield averaged across seasons, corroborating our first hypothesis. However, they struggled to distinguish the nuances between organic resources within the same quality (high- or low-quality class).

While the models did not fully capture the interannual yield variability, they better represented variability under full ISFM strategies (mineral N fertilizer plus high-quality organic resources), demonstrating

their usefulness for assessing fertilizer-related risks of intensification (e.g. Affholder, 1997; Rötter and Van Keulen, 1997; Traoré et al., 2023). However, they underestimated yield variability under low-quality organic resources without N fertilizer inputs, corroborating our second hypothesis. This discrepancy could arise from challenges in simulating N supply from soil organic matter, typical for agroecosystem models (e.g., Chilcott et al., 2007), coping strategies of maize to high N stress (Marenya et al., 2022), and the observed increase in water availability due to sawdust and maize stover application (Zhang et al., 2022). It may also result from amplified yield deviations over time, originating from small errors in simulated N supply compared to high-quality organic resources. Similar difficulties in simulating interannual variability have been reported in other studies in Kenya using LPJ-GUESS and DayCent (Ma et al., 2022; Nyawira et al., 2021) and even in a comparison of 16 different models (Couëdel et al., 2024).

The models captured reasonably well the effects of organic resources on SOC in clay-rich soils but underestimated SOC loss in sandy soils. The better performance in clay-rich soils aligns with Laub et al. (2024) where a Bayesian calibration of DayCent at the same sites led to the same difficulties with the clay-poor soils. In sandy soils, such as Machanga, erosion-induced SOC loss was significant but unaccounted for in the models. Incorporating erosion processes has been shown to reduce SOC modeling uncertainties in RothC and ICMB models (Chappell et al., 2016; Sanderman and Chappell, 2013). Relative effects on SOC between low- and high-quality organic resources were well reproduced in all soil types by both models, supporting their use for comparing organic resource additions of different qualities.

Overall, STICS and DayCent performed reasonably well and comparably to other tropical studies in simulating the effects of ISFM on crop yield and SOC with models based on the CENTURY structure (Kamoni et al., 2007; Ma et al., 2022; Nyawira et al., 2021). However, their performance was inferior to studies using STICS in temperate conditions, particularly in capturing the effects of contrasting organic resources (Levavasseur et al., 2021), suggesting that more research is needed on how SOM is stabilized in tropical soils. Overall, the capacity of both models to reproduce the effect of high- vs low-quality organic resources and mineral N shows their value for exploring ISFM strategies with high-quality organic resources versus ISFM with low-quality

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Model evaluations of DayCent and STICS. Observed versus simulated yearly yield change under five different organic inputs at 1.2 C ha⁻¹ + /-N (240 kg N ha⁻¹ yr⁻¹). Control= no organic resource applied, CL= *Calliandra calothyrsus* (CL), FYM=farmyard manure, MS= maize stover, SD=sawdust, TD= *Tithonia diversifolia*.</figcaption></figure>
organic resources, such as scenarios investigating the effect of higher
inputs of farmyard manure or high-quality compost (e.g Nezomba et al.,
2018). However, both models remain inadequate for simulating subtle
differences among low (e.g. MS vs. SD) and high-quality organic re-
sources (e.g. FYM vs. CC or TD) and thus can only be used to distinguish
the effect of high- and low-quality organic resources.

## 4.2. Similar model behavior despite different structure

Contrary to our initial third hypothesis, STICS and DayCent, despite their structural differences, demonstrated remarkably similar performance in grain yield, aboveground biomass and SOC simulations. This aligns with findings from studies comparing models of varying complexity (Couëdel et al., 2024; Falconnier et al., 2020; Zhang and Yu, 2021). Both models effectively represented the maize grain yield and AGB in Aludeka, similarly underpredicted yields and aboveground biomass in the -N treatment in Sidada, and overestimated the effects of mineral and organic N inputs on yield at Embu. Both models, generally struggled to simulate yield, aboveground biomass, and grain yield variability in -N treatments with low-quality or no organic resource inputs. The difficulty of representing yield and yield variability under low nitrogen (N) input conditions especially in the clay-rich soils with a lot of mineralizable N, suggests that the current model structure of both models (first-order kinetics) is insufficient to represent soil N mineralization (e.g., Chilcott et al. 2007). Yet, soil N mineralization is the primary source of N for plants in low-N environments. Plants, with in situ root exudates, can actively stimulate N mineralization through microbial priming (Jilling et al., 2021), which is not simulated in DayCent, STICS, or most other crop-soil models. Furthermore, N mineralization data is scarce since measuring N mineralization in the field is challenging (Hanselman et al., 2004). As a result, SOM turnover and N mineralization are often calibrated using long-term experimental data (e.g., Cécillon et al. 2018; Gurung et al. 2020), relying on proxies such as SOC loss for calibration, which is only partially correlated with soil N and which only change significantly in years to decades (Schrumpf et al., 2011). While this approach ensures that long-term balances are reasonably accurate, it provides no information on the short-term N mineralization rates that are critical for plant growth and may thus misrepresent them. Additionally, key soil properties influencing SOM stabilization and turnover and N mineralization, such as aluminum- and iron oxide abundance (von Fromm et al., 2025) and clay mineralogy (Reichenbach et al., 2021), both especially important in tropical soils, are not included in the models. Specifically, the dominance of 1:1 versus 2:1 clays has distinct effects on SOM stabilization (Von Fromm et al., 2023, Georgiou et al., 2025), with 1:1 clays stabilizing only about half the SOM of 2:1 clays (Six et al., 2024). Nevertheless, data on these properties are hardly available.

As our model comparison shows, models, despite differences in structures, remain fundamentally similar, i.e., often incorporating parts from one another. In fact, most SOM representations in agroecosystem models are either derived from RothC, which includes one to multiple active pools that turn over and one passive pool that does not (as in STICS), or from CENTURY, where pools represent a cascade with increasing turnover time from days to millennia. The primary limitation of both models may be that they rely on conceptual SOM pools with no direct measurable counterparts (Campbell and Paustian 2015). Moving towards “modeling the measurable” (Elliott et al., 1996), for example, by implementing a microbial pool that directly influences SOM decomposition and stabilization (Berardi et al., 2024; Chandel et al., 2023; Laub et al., 2024; Van de Broek et al., 2025; Zhang et al., 2024) or including measurable SOM pools such as mineral-associated organic matter vs. particulate organic matter (e.g., Abramoff et al., 2022; Tougma et al. 2025; Van de Broek et al., 2025), may thus be the way to go forward. Nevertheless, a recent study showed that these new-generation models are not necessarily more accurate in simulating carbon age (Brunmayr et al., 2024), calling for more studies that

implement and rigorously test these concepts in agroecosystem settings.

## 4.3. More complex model structures improve simulation accuracy only with additional data

Despite the generally very similar performance, our results provide some evidence for the hypothesis that the model with more realistic representation of key processes has a better performance. For example, STICS, with a more detailed simulation of phenology, crop maturity, and grain filling, generally outperformed DayCent in simulating yield and aboveground biomass during the evaluation phase, especially in the +N treatments. On the other hand, the higher number of SOM pools in DayCent did not lead to a better performance in SOC simulations. While most parameters of the soil and plant modules of DayCent and STICS cannot be directly compared due to different structures, the similar range of carbon use efficiencies, with 0.1–0.3 for DayCent and 0.2–0.35 for STICS, could be the reason for the similar performance of simulating SOC. It is also possible that DayCent’s more detailed representation of SOM was not supported by sufficient data (i.e., on pool distributions), preventing full utilization of its more complex structure. In that sense, both models are over-parameterized with regards to available SOM data, which usually leads to increased model error (Affholder et al., 2012; Passioura, 1996; Van Rompaey and Govers, 2002). Our results thus support the notion that model complexity improves performance only when sufficient data are available and over-fitting is avoided (Davoudabadi et al., 2024). Without sufficient data for calibration, more complex models may perform similarly or even worse than simpler models (Castañeda-Vera et al., 2015).

## 4.4. A stepwise calibration useful to highlight model strengths and weaknesses

The novelty of this study lies in its stepwise soil calibration of the most uncertain parameters, which allowed for a detailed assessment of the model's strengths and weaknesses in simulating key soil processes: (i) SOM turnover and (ii) carbon use efficiency of organic resources. Uncropped soil treatments, with and without organic resources, enabled the calibration independently of crop-related influences, such as C inputs, water use, and N uptake. To our knowledge, this use of experimental setup is novel for calibrating soil-crop models with contrasting organic resources under tropical conditions. Previously, long-term bare soil experiments without organic resources were used to calibrate the APSIM SoilN model in Australia (Dalal et al., 1995; Dimes, 1996; Probert et al., 1998), while organic resources application experiments were used to calibrate the CENTURY model in Sweden (Gerzabek et al., 1997; Paustian et al., 1992). However, our method has limitations, such as uncropped plots were sampled only once after the initial measurement (19 years for Embu and Machanga; 16 years for Aludeka and Sidada) and thus introducing potential biases and limiting the calibration of SOC simulation dynamics. Furthermore, the calibration and evaluation treatments could be more independent, as they differ solely in the quantity of organic resources applied (4 C ha⁻¹ yr⁻¹ during calibration and 1.2 C ha⁻¹ yr⁻¹ during evaluation) while being conducted at the same sites. This may limit the robustness of the evaluation and its applicability to broader contexts. Nevertheless, a recent study on the same dataset by Laub et al. (2024) showed that using a cross-evaluation approach, the performance of DayCent remained robust across sites and similar to the one in this study, suggesting that the issues rather depend on model structure than on calibration. Interestingly, the calibration for high organic resource quantity (4 C ha⁻¹ yr⁻¹) allowed reproducing grain yield from lower organic resource quantity (1.2 C ha⁻¹ yr⁻¹), which is a more representative of what farmers can afford (e.g Rusinamhodzi et al. 2013).

In addition to the missing soil nitrogen and soil water data discussed above, the absence of detailed crop data—such as leaf area index (LAI), plant nitrogen uptake, and in-season aboveground biomass dynamics,
limited our ability to accurately calibrate inter-annual yield variability. These variables are critical for capturing plant responses to interannual rainfall fluctuations. While the current stepwise calibration approach was useful in identifying the models' primary strengths and weaknesses, it did not achieve better accuracy than a recent Bayesian calibration of DayCent for the same sites (Laub et al., 2024). Nevertheless, this stepwise approach could be more effective when additional soil and crop data are available and could offer more insights into important processes. Conversely, when fewer variables are available, statistical calibration methods, such as Bayesian approaches, may offer a more appropriate alternative by optimizing parameter estimation within a probabilistic framework.

## 5. Conclusion

This study evaluated the STICS and DayCent models in their ability to simulate SOC dynamics and maize yields across contrasting organic resource amendments in tropical environments. Both models performed similarly, accurately capturing the effects of high-quality organic resources and mineral nitrogen fertilizers on SOC changes and maize yields in clay-rich soils, but struggled with SOC changes in sandy soils and yield variability under low-quality organic amendments. These findings align with broader challenges of applying soil-crop models elaborated in temperate regions to tropical conditions. Despite their limitations, the models are valuable tools for assessing ISFM strategies in sub-Saharan Africa, particularly when high-quality organic resources are used. Enhancing model performance will require using more detailed plant and soil in-season data and addressing processes like erosion and soil organic matter mineralization. This research emphasizes the need to adapt existing models to better reflect the unique conditions of tropical agroecosystems. Future work should aim to refine models by incorporating measurable SOM pools, microbial interactions, and erosion processes to enhance their relevance for tropical soils and climates.

## CRediT authorship contribution statement

**Bernard Vanlauwe:** Writing – review & editing, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.
**Daniel Mugendi:** Writing – review & editing, Resources, Methodology, Funding acquisition.
**Monicah Wanjiku Mucheru-Muna:** Writing – review & editing, Resources, Methodology, Funding acquisition.
**Carolina Remi:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.
**Gatien N. Falconnier:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization.
**Rindra Ranaivomanana:** Writing – review & editing, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation.
**Moritz Laub:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Antoine Couëdel:** Writing – original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Marc Corbeels:** Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.
**Johan Six:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

## 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.

## Acknowledgments

Part of the study was funded by the DSCATT project "Agricultural Intensification and Dynamics of Soil Carbon Sequestration in Tropical and Temperate Farming Systems" (No AF 1802-001, No 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 research has also been supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (grant no. 172940) and European's Horizon 2020 through the Societal Challenges program (grant no. 869367). We further acknowledge funding and technical support from Kenyatta University, the University of Embu, the Tropical Soil Biology and Fertility Institute of CIAT (TSBF-CIAT), the International Institute of Tropical Agriculture (IITA), and ETH Zurich for maintaining the experiments throughout many years.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.fcr.2025.110169.

## Data Availability

Data will be made available on request.

## References

* Abramoff, R.Z., Guenet, B., Zhang, H., Georgiou, K., Xu, X., Viscarra Rossel, R.A., Yuan, W., Giais, P., 2022. Improved global-scale predictions of soil carbon stocks with millennial version 2. Soil Biol. Biochem 164, 108466. https://doi.org/10.1016/j.soilbio.2021.108466.

* Affholder, F., 1997. Empirically modelling the interaction between intensification and climatic risk in semiarid regions. Field Crops Res 52, 79-93. https://doi.org/10.1016/S0378-4290(96)03453-3.

* Affholder, F., Tittonell, P., Corbeels, M., Roux, S., Motisi, N., Tixier, P., Wery, J., 2012. Ad hoc modeling in agronomy: what have we learned in the last 15 years? Agron. J. 104, 735-748. https://doi.org/10.2134/agronj2011.0376.

* Basso, B., Dumont, B., Maestrini, B., Shcherbak, I., Robertson, G.P., Porter, J.R., Smith, P., Paustian, K., Grace, P.R., Asseng, S., Bassu, S., Biernath, C., Boote, K.J., Cammarano, D., De Sanctis, G., Durand, J.-L., Ewert, F., Gayler, S., Hyndman, D.W., Kent, J., Martre, P., Nendel, C., Priesack, E., Ripoche, D., Ruane, A.C., Sharp, J., Thorburn, P.J., Hatfield, J.L., Jones, J.W., Rosenzweig, C., 2018. Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agric. Environ. Lett. 3, 180026. https://doi.org/10.2134/ael2018.05.0026.

* Beaudoin, N., Lecharpentier, P., Ripoche, D., Strullu, L., Mary, B., Léonard, J., Launay, M., Justes, É., 2023. STICS soil-crop model, conceptual framework, equations and uses. Versailles, Editions Quae.

* Berardi, D.M., Hartman, M.D., Brzostek, E.R., Bernacchi, C.J., DeLucia, E.H., von Haden, A.C., Kantola, I., Moore, C.E., Yang, W.H., Hudiburg, T.W., Parton, W.J., 2024. Microbial-explicit processes and refined perennial plant traits improve modeled ecosystem carbon dynamics. Geoderma 443, 116851. https://doi.org/10.1016/j.geoderma.2024.116851.

* Brisson, N.N., Launay, M., Mary, B.B., Beaudoin, N.N., 2009. Conceptual basis, formalisations and parameterization of the STICS crop model. Ed. Quae 304, 978-2-7592-0169-3.

* ten Berge, H.F.M., Hijbeek, R., van Loon, M.P., Rurinda, J., Tesfaye, K., Zingore, S., Craufurd, P., van Heerwaarden, J., Brentrup, F., Schröder, J.J., Boogaard, H.L., de Groot, H.L.E., van Ittersum, M.K., 2019. Maize crop nutrient input requirements for food security in sub-Saharan Africa. Glob. Food Sec. 23, 9-21. https://doi.org/10.1016/j.gfs.2019.02.001.

* Brunmayr, A.S., Hagedorn, F., Moreno Duborgel, M., Minich, L.I., Graven, H.D., 2024. Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models. Geosci. Model Dev. 17, 5961-5985. https://doi.org/10.5194/gmd-17-5961-2024.

* Campbell, E.E., Paustian, K., 2015. Current developments in soil organic matter modeling and the expansion of model applications: a review. Environ. Res. Lett. 10. https://doi.org/10.1088/1748-9326/10/12/123004.

* Cardinael, R., Guibert, H., Brédoumy, S.T.K., Gigou, J., N'Goran, K.E., Corbeels, M., 2022. Sustaining maize yields and soil carbon following land clearing in the forest-savannah transition zone of West Africa: results from a 20-year experiment. Field Crops Res 275, 108335. https://doi.org/10.1016/j.fcr.2021.108335.

* Castaneda-Vera, A., Leffelaar, P.A., Álvaro-Fuentes, J., Cantero-Martínez, C., Mínguez, M.I., 2015. Selecting crop models for decision making in wheat insurance. Eur. J. Agron. 68, 97-116. https://doi.org/10.1016/j.eja.2015.04.008.

* Cécillon, L., Gaudin, F., Chenu, C., Houot, S., Jolivet, R., Kätterer, T., Lutfalla, S., Macdonald, A., Van Oort, F., Plante, A.F., Savignac, F., Soucémarianadin, L.N.,
Barré, P., 2018. A model based on Rock-Eval thermal analysis to quantify the size of the centennial persistent organic carbon pool in temperate soils. Biogeosciences 15, 2835-2849. https://doi.org/10.1594/bg-15-2835-2018.

Chandel, A.K., Jiang, L., Luo, Y., 2023. Microbial models for simulating soil carbon dynamics: a review. J. Geophys. Res. Biogeosciences 128. https://doi.org/10.1029/2023JG007436.

Chappell, A., Baldock, J., Sanderman, J., 2016. The global significance of omitting soil erosion from soil organic carbon cycling schemes. Nat. Clim. Chang. 6, 187-191. https://doi.org/10.1038/nclimate2829.

Chilcott, C.R., Dalal, R.C., Parton, W.J., Carter, J.O., King, A.J., 2007. Long-term trends in fertility of soils under continuous cultivation and cereal cropping in Southern Queensland. IX.<xref Ref. "FN".Rid. = "FN1".*<xref>. Simul. Soil Carbon Nitrogen pools Using CENTURY Model. Soil Res 45, 206-217.

Chivenge, P., Vanlauwé, B., Gentile, R., Wangechi, H., Mugendi, D., van Kessel, C., Six, J., 2009. Organic and mineral input management to enhance crop productivity in central Kenya. Agron. J. 101, 1266-1275. https://doi.org/10.2134/agronj2008.0188x.

Cobo, J.G., Dercon, G., Cadisch, G., 2010. Nutrient balances in African land use systems across different spatial scales: a review of approaches, challenges and progress. Agric. Ecosyst. Environ. 136, 1-15. https://doi.org/10.1016/j.agee.2009.11.006.

Couëld, A., Falconnier, G.N., Adam, R., Cardinael, R., Boote, K., Justes, E., Smith, W.N., Whitbread, A.M., Affholder, F., Balkow, J., Basso, B., Bhatia, A., Chakrabarti, B., Chikowo, R., Christina, M., Faye, B., Ferchaud, F., Folberth, A., Akinseye, F.M., Gaiser, T., Galdos, M., Gayler, S., Gorooeii, A., Grant, B., Guibert, H., Hoogenboom, G., Kamali, B., Laub, M., Maureira, F., Mequantin, F., Nendel, C., Porter, C.H., Ripoche, D., Ruane, A.C., Rusinamhodzi, L., Sharma, S., Singh, U., Six, J., Srivastava, A., Vanlauwé, B., Versini, A., Vianna, M., Webber, H., Weber, T., Zhang, C., Corbeels, M., 2024. Long term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa. Eur. J. Agron. 155. https://doi.org/10.1016/j.eja.2024.127109.

Dalal, R.C., Strong, W.M., Weston, E.J., Cooper, J.E., Lehane, K.J., King, A.J., Chicken, C. J., 1995. Sustaining productivity of a vertisol at warra, Queensland, with fertilisers, no-tillage, or legumes. 1. organic matter status. Aust. J. Exp. Agric. 35, 903-913.

Davoudabadi, M.J., Pagendam, D., Drovandi, C., Baldock, J., White, G., 2024. Innovative approaches in soil carbon sequestration modelling for better prediction with limited data. Sci. Rep. 14, 1-13. https://doi.org/10.1038/s41598-024-53516-z.

Del Grosso, S., Parton, W., Mosier, A., Hartman, M., Brenner, J., Ojima, D., Schimel, D., 2001. Simulated interaction of carbon dynamics and nitrogen trace gas fluxes using the DAYCENT model. Soil Carbon Nitrogen Dyn. Soil Manag. https://doi.org/10.1201/9781420032635.ch8.

Dimes, J., 1996. Simulation of mineral N supply to no-till crops in the semi arid tropics. Griffith University, Queensland.

Doetterl, S., Stevens, A., Six, J., Merckx, R., Van Oost, K., Casanova-Pinto, M., Casanova-Katny, A., Muñoz, C., Boudin, M., Zagal Venegas, E., Boeckx, P., 2015. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780-783. https://doi.org/10.1038/ngeo2516.

Elliott, E.T., Paustian, K., Frey, S.D., 1996. Modeling the measurable or measuring the modelable: a hierarchical approach to isolating meaningful soil organic matter fractionations. In: Powlson, D.S., Smith, P., Smith, J.U. (Eds.), Evaluation of Soil Organic Matter Models. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 161-179.

Falconnier, G.N., Corbeels, M., Boote, K.J., Affholder, F., Adam, R., MacCarthy, D.S., Ruane, A.C., Nendel, C., Whitbread, A.M., Justes, É., Ahuja, L.R., Akinseye, F.M., Alou, I.N., Amouzou, K.A., Anapalli, S.S., Baron, C., Basso, B., Baudron, F., Bertuzzi, P., Challinor, A.J., Chen, Y., Deryng, D., Elsayed, M.L., Faye, B., Gaiser, T., Galdos, M., Gayler, S., Gerardeaux, E., Giner, M., Grant, B., Hoogenboom, G., Ibrahim, E.S., Kamali, B., Kersebaum, K.C., Kim, S.H., van der Laan, M., Leroux, L., Lizaso, J.I., Maestriini, B., Meier, A.E., Mequantin, F., Ndoli, A., Porter, C.H., Priesack, E., Ripoche, D., Sida, T.S., Singh, U., Smith, W.N., Srivastava, A., Sinha, S., Tao, F., Thorburn, P.J., Timlin, D., Traore, B., Twine, T., Webber, H., 2020. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa. Glob. Chang. Biol. 1-23. https://doi.org/10.1111/gcb.15261.

Falconnier, G.N., Cardinael, R., Corbeels, M., Baudron, F., Chivenge, P., Couëld, A., Ripoche, A., Affholder, F., Naudin, K., Benaillon, E., Rusinamhodzi, L., Leroux, L., Vanlauwé, B., Giller, K.E., 2023. The input reduction principle of agroecology is wrong when it comes to mineral fertilizer use in sub-Saharan Africa. Outlook Agric. 52, 311-326. https://doi.org/10.1177/00307270231199795.

FAOSTAT, 2023. Food and agriculture organization of the united nations [WWW Document]. FAO.

Frey, S.D., Lee, J., Melillo, J.M., Six, J., 2013. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Chang. 3, 395-398. https://doi.org/10.1038/nclimate1796.

Gaillard, R.K., Jones, C.D., Ingraham, P., Collier, S., Izaurralde, R.C., Jokela, W., Osterholz, W., Salas, W., Vadas, P., Ruark, M.D., 2018. Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models. Ecol. Appl. 28, 694-708. https://doi.org/10.1002/eap.1674.

Gauch, H.G., Hwang, J.T.G., Fick, G.W., 2003. Model Evaluation by Comparison of Model-Based Predictions and Measured Values. Agron. J. 95, 1442-1446. https://doi.org/10.2134/agronj2003.1442.

Georgiou, K., Angers, D., Champiny, R.E., Cotrufo, M.F., Craig, M.E., Doetterl, S., Grandy, A.S., Lavallee, J.M., Lin, Y., Lugato, E., Poeplau, C., Rocci, K.S., Schweizer, S.A., Schweizer, S.A., Six, J., Wieder, W.R., 2025. Soil Carbon Saturation: What Do We Really Know? Glob. Chang. Biol. 31. https://doi.org/10.1111/gcb.70197.

Gentile, R., Vanlauwé, B., van Kessel, C., Six, J., 2009. Managing n availability and losses by combining fertilizer-N with different quality residues in Kenya. Agric. Ecosyst. Environ. 131, 308-314. https://doi.org/10.1016/j.agee.2009.02.003.

Gentile, R., Vanlauwé, B., Chivenge, P., Six, J., 2011. Trade-offs between the short- and long-term effects of residue quality on soil c and n dynamics. Plant Soil 338, 159-169. https://doi.org/10.1007/s11104-010-0360-z.

Georgiou, M.H., Malhotra, A., Wieder, W.R., Ennis, J.H., Hartman, M.D., Sulander, B.N., Berhe, A.A., Grandy, A.S., Kyker-Snowman, E., Lajtha, K., Moore, J.A.M., Pierson, D., Jackson, R.B., 2021. Divergent controls of soil organic carbon between observations and process-related models. Biogeochemistry 156, 5-17. https://doi.org/10.1007/s10533-021-00819-2.

Gerbazek, M.H., PICHLMAYER, F., KIRCHMANN, H., HABERHAUER, G., 1997. The response of soil organic matter to manure amendments in a long-term experiment at ultima, Sweden. Eur. J. Soil Sci. 48, 273-282. https://doi.org/10.1111/j.1365-2389.1997.tb00547.x.

Gurung, R.B., Ogle, S.M., Breidt, F.J., Williams, S.A., Parton, W.J., 2020. Bayesian calibration of the DayCent ecosystem model to simulate soil organic carbon dynamics and reduce model uncertainty. Geoderma 376, 114529. https://doi.org/10.1016/j.geoderma.2020.114529.

Hanselman, T.A., Graetz, D.A., Obreza, T.A., 2004. A comparison of in situ methods for measuring net nitrogen mineralization rates of organic soil amendments. J. Environ. Qual. 33, 1098-1105. https://doi.org/10.2134/eqj2004.1098.

Hartman, M.D., Parton, W.J., Grosso, S.J., Del, Easter, M., Hendryx, J., Hilinski, T., Kelly, R., Keough, C.A., Killian, K., Lutz, S., Marx, E., McKeown, R., Ogle, S., Ojima, D.S., Paustian, K., Swan, A., Williams, S., 2020. DayCent Ecosystem Model: User Manual, Scientific Basis, and Technical Documentation. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO.

IPCC, 2023. Summary for Policymakers: Synthesis Report., Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.

Jilling, A., Keiluweit, M., Gutknecht, J.L.M., Grandy, A.S., 2021. Priming mechanisms providing plants and microbes access to mineral-associated organic matter. Soil Biol. Biochem 158, 108265. https://doi.org/10.1016/j.soilbio.2021.108265.

Kamoni, P.T., Gicheru, P.T., Wokabi, S.M., Easter, M., Milne, E., Coleman, K., Faloon, P., Paustian, K., Killian, K., Kihanda, F.M., 2007. Evaluation of two soil carbon models using two Kenyan long term experimental datasets. Agric. Ecosyst. Environ. 122, 95-104. https://doi.org/10.1016/j.agee.2007.01.011.

Laub, M., Corbeels, M., Mathu Ndungu, S., Mucheru-Muna, M.W., Mugendi, D., Necpalova, M., Van de Broek, M., Waswa, W., Vanlauwé, B., Six, J., 2023b. Combining manure with mineral n fertilizer maintains maize yields: evidence from four long-term experiments in Kenya. Field Crops Res 291, 108788. https://doi.org/10.1016/j.fcr.2022.108788.

Laub, M., Corbeels, M., Couëld, A., Ndungu, S.M., Mucheru-muna, M.W., Mugendi, D., Necpalova, M., Waswa, W., 2023a. Managing soil organic carbon in tropical agroecosystems: evidence from four long-term experiments in Kenya. SOIL 301-323.

Laub, M., Necpalova, M., Van De Broek, M., Corbeels, M., Ndungu, S.M., Mucheru-Muna, M.W., Mugendi, D., Yegon, R., Waswa, W., Vanlauwé, B., Six, J., 2024. Modeling integrated soil fertility management for maize production in Kenya using a Bayesian calibration of the DayCent model. Biogeosciences 21, 3691-3716. https://doi.org/10.5194/bg-21-3691-2024.

Levavasseur, F., Mary, B., Houot, S., 2021. C and n dynamics with repeated organic amendments can be simulated with the STICS model. Nutr. Cycl. Agroecosystems 5. https://doi.org/10.1007/s10705-020-10106-5.

Loague, K., Green, R.E., 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7, 51-73. https://doi.org/10.1016/0169-7722(91)90038-3.

Ma, J., Rabin, S.S., Anthoni, P., Bayer, F.D., Nyawira, S.S., Olin, S., Xia, L., Arneth, A., 2022. Assessing the impacts of agricultural managements on soil carbon stocks, nitrogen loss and crop production - a modelling study in Eastern Africa. Biogeosciences 2022 1-31.

Marenya, P.P., Wanyama, R., Alemu, S., Woyengo, V., 2022. Building resilient maize production systems with Stress-Adapted varieties: Farmers' priorities in Western Kenya. Front. Sustain. Food Syst. 6, 1-13. https://doi.org/10.3389/fsufs.2022.702405.

Nezomba, H., Mtambanengwe, F., Rurinda, J., Mapfumo, P., 2018. Integrated soil fertility management sequences for reducing climate risk in smallholder crop production systems in Southern Africa. Field Crops Res 224, 102-114. https://doi.org/10.1016/j.fcr.2018.05.003.

Nissan, A., Alcolombru, U., Peleg, N., Galili, N., Jimenez-Martinez, J., Molnar, P., Holzner, M., 2023. Global warming accelerates soil heterotrophic respiration. Nat. Commun. 14. https://doi.org/10.1038/s41467-023-38981-w.

Nyawira, S.S., Hartman, M.D., Nguyen, T.H., Margenot, A.J., Kihara, J., Paul, B.K., Williams, S., Bolo, P., Sommer, R., 2021. Simulating soil organic carbon in maize-based systems under improved agronomic management in Western Kenya. Soil Tillage Res 211, 105000. https://doi.org/10.1016/j.still.2021.105000.

Palm, C.A., Gachengo, C.N., Delve, R.J., Cadisch, G., Giller, K.E., 2001. Organic inputs for soil fertility management in tropical agroecosystems: application of an organic resource database. Agric. Ecosyst. Environ. 83, 27-42. https://doi.org/10.1016/S0167-8809(00)00267-X.

Parton, W.J., Schimel, D.S., Cole, C.V., Ojima, D.S., 1987. Analysis of factors controlling soil organic matter levels in great plains grasslands. Soil Sci. Soc. Am. J. 51, 1173-1179. https://doi.org/10.2136/ssaj1987.03615995005100050015x.

Passioura, J.B., 1996. Simulation models: science, Snake oil, education, or engineering? Agron. J. 88, 690-694. https://doi.org/10.2134/agronj1996.00021926200880050002x.
Paustian, K., Parton, W.J., Persson, J., 1992. Modeling soil organic matter in Organic-Amended and Nitrogen-Fertilized Long-Term plots. Soil Sci. Soc. Am. J. 56, 476-488. https://doi.org/10.2136/ssaj1992.03615995005600020023x.

Probert, M.E., Dimes, J.P., Keating, B.A., Dalal, R.C., Strong, W.M., APSIM's water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agric. Syst. 56, 1-28. https://doi.org/10.1016/S0308-521X(97)00028-0.

Rattalino Edreira, J.I., Andrade, J.F., Cassman, K.G., van Ittersum, M.K., van Loon, M.P., Grassini, P., 2021. Spatial frameworks for robust estimation of yield gaps. Nat. Food 2, 773-779. https://doi.org/10.1038/s43016-021-00365-y.

Reichenbach, M., Fiener, P., Garland, G., Griepentrog, M., Six, J., Doetterl, S., 2021. The role of geochemistry in organic carbon stabilization against microbial decomposition in tropical rainforest soils. Soil 7, 453-475. https://doi.org/10.5194/soil-7-453-2021.

Rezaei, E.E., Webber, H., Asseng, S., Boote, K., Durand, J.L., Ewert, F., Martre, P., MacCarthy, D.S., 2023. Climate change impacts on crop yields. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-023-00491-0.

Rompaey, A.J.J., Govers, G., 2002. Data quality and model complexity for regional scale soil erosion prediction. Int. J. Geogr. Inf. Sci. 16, 663-680. https://doi.org/10.1080/13658810210148561.

Rötter, R., Van Keulen, H., 1997. Variations in yield response to fertilizer application in the tropics: II. Risks and opportunities for smallholders cultivating maize on Kenya's arable land. Agric. Syst. 53, 69-95. https://doi.org/10.1016/S0308-521X(96)00037-6.

Rusinamhodzi, L., Corbeels, M., Zingore, S., Nyamangara, J., Giller, K.E., 2013. Pushing the envelope? Maize production intensification and the role of cattle manure in recovery of degraded soils in smallholder farming areas of Zimbabwe. Field Crops Res 147, 40-53. https://doi.org/10.1016/j.fcr.2013.03.014.

Sanchez, P.A., 2002. Soil fertility and hunger in Africa. Science (80.) 295, 2019-2020. https://doi.org/10.1126/science.1065256.

Sanderman, J., Chappell, A., 2013. Uncertainty in soil carbon accounting due to unrecognized soil erosion. Glob. Chang. Biol. 19, 264-272. https://doi.org/10.1111/gcb.12030.

Schrumpf, M., Schulze, E.D., Kaiser, K., Schumacher, J., 2011. How accurately can soil organic carbon stocks and stock changes be quantified by soil inventories? Biogeosciences 8, 1193-1212. https://doi.org/10.5194/bg-8-1193-2011.

Six, J., Doetterl, S., Laub, M., Müller, C.R., Van De Broek, M., 2024. The six rights of how and when to test for soil c saturation. Soil 10, 275-279. https://doi.org/10.5194/soil-10-275-2024.

Sow, S., Senghor, Y., Sadio, K., Vezy, R., Roupsard, O., Affholder, F., N'dienor, M., Clermont-Dauphin, C., Gaglo, E.K., Ba, S., Tounkara, A., Balde, A.B., Agbohessou, Y., Seghieri, J., Sall, S.N., Couedel, A., Leroux, L., Jourdan, C., Diaite, D.S., Falconnier, G.N., 2024. Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth. Field Crops Res 306, 109206. https://doi.org/10.1016/j.fcr.2023.109206.

Tiltonell, P., Giller, K.E., 2013. When yield gaps are poverty traps: the paradigm of ecological intensification in African smallholder agriculture. Field Crops Res 143, 76-90. https://doi.org/10.1016/j.fcr.2012.10.007.

Tougma, I.A., Van de Broek, M., Six, J., Gaiser, T., Holz, M., Zentgraf, I., Webber, H., 2025. AMPSOM: a measureable pool soil organic carbon and nitrogen model for arable cropping systems. Environ. Model. Softw. 185. https://doi.org/10.1016/j.envsoft.2024.106291.

Traoré, A., Falconnier, G.N., Ba, A., Sissoko, F., Sultan, B., Affholder, F., 2022. Modeling sorghum-cowpea intercropping for a site in the savannah zone of Mali: strengths and

weaknesses of the stics model. Field Crops Res 285. https://doi.org/10.1016/j.fcr.2022.108581.

Traoré, A., Falconnier, G.N., Couëdel, A., Sultan, B., Chimonyo, V.G.P., Adam, M., Affholder, F., 2023. Sustainable intensification of sorghum-based cropping systems in semi-arid sub-Saharan Africa: the role of improved varieties, mineral fertilizer, and legume integration. Field Crops Res 304. https://doi.org/10.1016/j.fcr.2023.109180.

Van de Broek, M., Govers, G., Schrumpf, M., Six, J., 2025. A microbially-driven and depth-explicit soil organic carbon model constrained by carbon isotopes to reduce equifinality. Biogeosciences 1427-1446.

Van Ittersum, M.K., Van Bussel, L.G.J., Wolf, J., Grassini, P., Van Wart, J., Guilpart, N., Claessens, L., De Groot, H., Wiebe, K., Mason-D'Croz, D., Yang, H., Boogaard, H., Van Oort, P.A.J., Van Loon, M.P., Saito, K., Adimo, O., Adjei-Nsiah, S., Agali, A., Bala, A., Chikowo, R., Kaizzi, K., Kouressy, M., Makoi, J.H.J.R., Ouattara, K., Tesfaye, K., Cassman, K.G., 2016. Can sub-Saharan Africa feed itself? Proc. Natl. Acad. Sci. U. S. A. 113, 14964-14969. https://doi.org/10.1073/pnas.1610359113.

Vanlauwe, B., Bationo, A., Chianu, J., Giller, K.E., Merckx, R., Mokwunye, U., Ohiokpehai, O., Pypers, P., Tabo, R., Shepherd, K.D., Smaling, E.M.A., Woomer, P.L., Sanginga, N., 2010. Integrated soil fertility management: operational definition and consequences for implementation and dissemination. Outlook Agric. 39, 17-24. https://doi.org/10.5367/000000010791169998.

von Fromm, S.F., Jungkunst, H.F., Amenkhienan, B., Hall, S.J., Georgiou, K., Pries, C.H., Montano-López, F., Quesada, C.A., Rasmussen, C., Schrumpf, M., Singh, B., Thompson, A., Wagai, R., Fiedler, S., 2025. Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale. Biogeochemistry 168, 1-17. https://doi.org/10.1007/s10533-025-01208-9.

Von Fromm, S.F., Hoyt, A.M., Lange, M., Acquah, G.E., Aynekulu, E., Berhe, A.A., Haefele, S.M., Mcgrath, S.P., Shepherd, K.D., Sila, A.M., Six, J., Towett, E.K., Trumbore, S.E., Vägen, T.G., Weullow, E., Winowiecki, L.A., Doetterl, S., 2021. Continental-scale controls on soil organic carbon across sub-Saharan Africa. Soil 7, 305-332. https://doi.org/10.5194/soil-7-305-2021.

Von Fromm, S.F., Doetterl, S., Butler, B., Aynekulu, E., Berthe, A.A., Haefele, S.M., Mcgrath, S.P., Shepherd, K.D., Six, J., Tamene, L., Tondoh, E.J., Vägen, T.-G., Winowiecki, L.A., Trumbore, S.E., Hoyt, A.M., 2023. Controls on timescales of soil organic carbon persistence across sub-Saharan Africa. Glob. Chang. Biol. 18. https://doi.org/10.1111/gcb.17089.

Yin, X., Kersebaum, K.C., Kollas, C., Baby, S., Beaudoin, N., Manevski, K., Palosuo, T., Nendel, C., Wu, L., Hoffmann, M., Hoffmann, H., Sharif, B., Armas-Herrera, C.M., Bindi, M., Charfeddine, M., Conradt, T., Constantin, J., Ewert, F., Ferrise, R., Gaiser, T., de Cortazar-Atauri, I.G., Giglio, L., Hlavinka, P., Lana, M., Launay, M., Louarn, G., Manderscheid, R., Mary, B., Mirschel, W., Moriondo, M., Öztürk, I., Pacholski, A., Ripoche-Wachter, D., Rötter, R.P., Ruget, F., Trnka, M., Ventrella, D., Weigel, H.J., Olesen, J.E., 2017. Multi-model uncertainty analysis in predicting grain n for crop rotations in Europe. Eur. J. Agron. 84, 152-165. https://doi.org/10.1016/j.eja.2016.12.009.

Zhang, J., Fu, G., Jin, Z., Chi, L., Xu, G., Yue, D., 2022. Optimization of organic mulches thickness improves soil moisture retention under controlled conditions. PhytonInt. J. Exp. Bot. 91, 841-857. https://doi.org/10.32604/phyton.2022.017938.

Zhang, Y., Yu, Q., 2021. Does agroecosystem model improvement increase simulation accuracy for agricultural N2O emissions? Agric. For. Meteor. 297, 108281. https://doi.org/10.1016/j.agrformet.2020.108281.

Zhang, Y., King, A.E., Hamilton, E., Cotrufo, M.F., 2024. Representing cropping systems with the MEMS 2 ecosystem model. Agron. J. 2328-2345. https://doi.org/10.1002/agj2.21611.