# Modeling perennial sugarcane growth with the STICS soil-crop model in contrasted environments: Assessment of accuracy and robustness

Lelenda Florent Kebalo, Antoine Versini, Jean-Christophe Soulie, Marion

Ramos, Léa Chevalier, Maxime Chaput, Mathias Christina

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## To cite this version:

Lelenda Florent Kebalo, Antoine Versini, Jean-Christophe Soulie, Marion Ramos, Léa Chevalier, et al. Modeling perennial sugarcane growth with the STICS soil-crop model in contrasted environments: Assessment of accuracy and robustness. Field Crops Research, 2025, 333, pp.110100. 10.1016/j.fcr.2025.110100. hal-05246493

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# Modeling perennial sugarcane growth with the STICS soil-crop model in contrasted environments: Assessment of accuracy and robustness

Lelenda Florent Kebalo<sup>a,b,c,d,*</sup> ◆, Antoine Versini<sup>a,b</sup>, Jean-Christophe Soulie<sup>b,f</sup>, Marion Ramos<sup>a,b,c,d,e</sup>, Léa Chevalier<sup>a,b,c,d,e</sup>, Maxime Chaput<sup>a,b,c,d</sup>, Mathias Christina<sup>c,d</sup>

## ARTICLE INFO

**Keywords:**
Perennial crop
Giant grass
Crop model
Uncertainty
Cultivar

## ABSTRACT

Sugarcane holds significant economic importance due to its role in bioenergy and sugar production worldwide. However, as global demand increases, research is needed to identify the best management practices for sustainable yield improvement, and modeling approaches can be helpful. Our study aimed to parameterize and validate the STICS soil-crop model for sugarcane growth in an extensive range of environments. This model is not sugarcane specific but allows for the simulation of all the components of the carbon and nitrogen cycles and is useful for many research studies. The parameterization dataset included 20 crop cycles with Reunion Island varieties (mainly R570), and the validation dataset included 62 crop cycles with R570 variety and 158 crop cycles with 30 international varieties. The model was parameterized with belowground (root length, biomass, and perennial reserve) and aboveground variables, including leaf area index, intercepted radiation, and biomass components (stem, leaves). Validation was performed on aboveground variables. The model satisfactorily simulated all aboveground variables except for leaf area and mass, which were less accurately simulated. Despite being parameterized only on Reunion Island varieties, the model was robust enough to simulate some above-ground biomass at harvest for many international varieties. Additionally, the climatic domain of validity of the model was evaluated, and error on biomass at harvest increased for both high and low-yielded environments (high or low water availability and temperature), indicating that future improvements could be carried out in a complementary work. The STICS model can be used with good robustness to simulate sugarcane growth under similar varieties and pedoclimatic conditions, allowing exploration of management strategies that can boost productivity while maintaining the sustainability of cropping systems.

## 1. Introduction

Sugarcane production worldwide was estimated at about 2 billion tonnes of fresh matter in 2022, covering almost 27 million hectares (FAO, 2024). This crop is grown mainly for sugar and bioenergy production (Cabral et al., 2020). The three leading producers are Brazil, India, and China, with annual production of 724, 439, and 103 million tonnes, respectively, accounting for more than 60 % of the global output (FAO, 2024). Although sugarcane production in some small tropical

islands, such as the Indian Ocean, Caribbean, or French West Indies, is low relative to global production, its role in the economies of these areas is highly valuable, providing income and jobs (Dumont et al., 2022).

With the growing demand for sugar (FAO-OECD, 2023) and alternatives to fossil-fuel energy sources (Amezcua-Allieri et al., 2019), sugarcane cultivation has expanded in many countries, including the leading producers (Hernandes et al., 2022). However, challenges such as climate change, soil variability, certain management practices like the removal of crop residues from the farm, and mechanical harvesting have

<footnote><sup>a</sup> CIRAD, UPR Recyclage et risque, Montpellier 34000, France</footnote>

<footnote><sup>b</sup> Recyclage et Risque, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>c</sup> CIRAD, UPR Aïda, Montpellier 34000, France</footnote>

<footnote><sup>d</sup> Aïda, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>e</sup> eRcane, Saint-Denis, La Réunion, France</footnote>

<footnote><sup>f</sup> CIRAD, UPR Recyclage et Risque, La Réunion, Saint-Denis F-97743, France</footnote>

<footnote>* Correspondence to: 01 Avenue Agropolis, Montpellier 34398 Cedex 5, France.
E-mail address: lelenda_florent.kebalo@cirad.fr (L.F. Kebalo).</footnote>
negatively affected the regrowth ability of sugarcane in some regions (Delmond et al., 2024; Dietrich et al., 2017). These issues have led to reduced yields and raised concerns about the potential productivity in new cultivation areas with different soil and climate conditions (Christina et al., 2024; Jaiswal et al., 2023; Liang et al., 2020).

In this context, process-based crop models are essential tools for supporting the adoption of novel management practices while minimizing losses and enhancing yields (Di Paola et al., 2016; Keating and Thorburn, 2018; Singels, 2013). Several crop models have been used to assess management strategies for various crops (Autret et al., 2020; Bécel et al., 2015; Dias et al., 2021). Recent research evaluated the performance of sugarcane models such as DSSAT-Canegro, Mosicas, and APSIM-Sugar (Jones et al., 2021; Marin et al., 2015; Peng et al., 2020; Verma et al., 2023) or ECOSMOS (Colmanetti et al., 2024), confirming their ability to successfully simulate growth, while recommending sometimes recalibration to increase model accuracy. Nevertheless, compared with other major crops (e.g. cereals), the number of models available for sugarcane is still limited (Singels, 2013), and recent work on model comparison highlighted the importance of working with a wide range of crop models to limit the biases inherent in the choice of certain formalisms (Falconnier et al., 2020).

Given the limited number of sugarcane models, there is a need to develop additional robust and generic models, in addition to existing ones such as APSIM-Sugar and DSSAT-CANEGRO (Keating et al., 1999; Thorburn et al., 2005; Marin et al., 2015). The STICS soil-crop model shows a strong potential in this regard due to its ability to simulate crop growth and explore the interaction between an extensive range of management practices and all components of the carbon, nitrogen, and water cycles. Similar to APSIM-Sugar and DSSAT-CANEGRO, STICS model is known for its robustness and genericity; it can simulate the interaction between soil, climate and crop management on plant growth for a wide variety of crops (Beaudoin et al., 2022), nitrogen fluxes (e.g., nitrous oxide emissions, ammonia volatilization, nitrate lixiviation), and its consequence in terms of long-term evolution of soil organic matter, carbon and nitrogen cycles (Autret et al., 2020). Moreover, it features a perennial reserve function (in its version 10) that allows for the simulation of plant regrowth (e.g., Miscanthus, Strullu et al., 2014), which is crucial for modeling multi-year sugarcane crop regrowth. STICS also accommodates a diverse range of fertilizers, including organic ones (Levavasseur et al., 2022) and crop residues (Justes et al., 2009), providing a better understanding and prediction of crop responses to management practice changes.

Despite the considerable potential of the STICS model, several critical gaps need to be addressed to use its capabilities thoroughly. One of the main challenges is the parameterization of the model specifically for sugarcane. To date, the STICS model for sugarcane has not been parameterized and validated. In addition, sugarcane is a semi-perennial crop typically harvested annually without uprooting. The belowground biomass, including the stool and roots, is the source of new shoots (ratoons) the next season's crop, and its simulation over crop cycles and ratoons is challenging, as biases could occur in medium- or long-term simulations if C and N reserves are not well simulated. Despite the crucial role of the root system in a perennial crop like sugarcane, research on this compartment is scarce (Poultney et al., 2024; Versini et al., 2020) and generally poorly explored in modeling studies. Additionally, understanding the functioning of the perennial reserves, which allow the plant to regrow as ratoons, is challenging, particularly when it comes to accurately identifying and quantifying the belowground organs involved (Chevalier et al., 2023b).

Although models can accurately simulate crop development when adequately parameterized, their validity domain might be limited, especially under varying climatic conditions and crop varieties. These limits are particularly true when a large number of parameters are mathematically optimized in specific conditions. This highlights the importance of exploring the genericity and validity domain of the calibration of sugarcane for a given model, i.e., the climatic situations and

varieties under which the model's outputs are considered relevant, reliable, robust, and sufficiently accurate (Vezy et al., 2023b).

The objectives of this study were i) to parameterize and validate the sugarcane growth within the STICS model, which includes formalisms for perennial crops in its v10 version. This parameterization will focus on capturing the crop's response to climatic variability, ensuring that the model accurately reflects the physiological and phenological processes of sugarcane under different environmental conditions, and ii) to assess the accuracy of the sugarcane parameterization and the robustness of the model over a wide range of pedoclimatic conditions. This will involve analyzing the model errors, within a large range of varieties, rainfall and temperature over the study sites to assess if a precise parameterization variety by variety would be needed to obtain correct performances.

## 2. Material and methods

### 2.1. General description of the STICS soil-crop model

The STICS process-based model simulates crop development, growth, and yield, along with carbon, water, and nitrogen dynamics in the soil-crop system, on a daily time-step over one or more cycles (Brisson et al., 2003). The model's inputs include meteorological data, crop management practices, soil parameters, crop-specific characteristics, and the system's initial state, such as the water and nitrogen content of each soil layer or the condition of roots and perennial organs, for perennial crops. In this paper, we describe only the aspects of the model relevant to the sugarcane crop. Further details, including all equations, are described in the open-source book of the STICS model (Beaudoin et al., 2022).

#### 2.1.1. Sugarcane shoot growth and perennial reserves in STICS

Plant development is driven by a thermal index (degree-days), which enables the plant to progress through different phenological stages. These stages include vegetative and reproductive phases. However, only the vegetative stages were considered for sugarcane, as flowering, though it can occur before harvest, typically happens late in the cycle. Consequently, sugarcane yield in the model is defined as stem yield. Overall, sugarcane growth is driven by the plant's carbon accumulation. Photosynthetically Active Radiation (PAR) is intercepted by the leaves and converted into biomass using daily Radiation Use Efficiency (RUE), according to Beer-Lambert's law and Monteith's equation. Daily calculations of water, carbon, and nitrogen balances within the soil-plant system allow the simulation of water and nitrogen stress indices, which impact plant growth. The model distinguishes between sugarcane biomass in non-perennial organs (stem and leaves) and perennial organs (stool, in sugarcane case). Sugarcane can remobilize part of the biomass and nitrogen stored in its perennial organs during ratoon growth. The model also simulates the daily senescence of perennial organs.

#### 2.1.2. Sugarcane root growth in STICS

In the STICS model, roots primarily function as absorbers of water and mineral nitrogen, and they are described based on their front depth and density profile. They also serve as a sink for assimilated carbon through biomass allocation. The STICS model offers two options for root simulation: a standard profile or a true density profile. For sugarcane, we used the true density option, which allows dynamic simulation of root biomass in direct relation to root length expansion, distinguishing between two types of roots (coarse and fine roots), each with its own root turnover rate. The true density option separates root expansion from root senescence based on thermal time, enabling the calculation of a root density profile comparable to field measurements. In the true density option, root expansion can be either dependent on or independent of shoot biomass. We used the option where the sugarcane root system is linked to aerial biomass, meaning the nutritional functions of the root system are calculated based on supply and demand principles. The
demand arises from shoot metabolism, while supply is determined by soil nutrient availability and the root system's capacity to capture those elements. Root length expansion is driven by a proportion of allocated biomass from shoot growth, which can change over sugarcane growth (continuous link option).

## 2.2. Dataset source

### 2.2.1. Belowground parameterization dataset

The belowground parameterization dataset, including observations of sugarcane belowground developments (roots and perennial organs), was collected from published experiments in Reunion Island (Table 1). The first dataset (Root_PC) included observations in plant crop of total root length (TRL) and distribution during a one-year growth cycle, up to 1.2 m depth (root front of this study), in a trial with optimal fertilization and irrigation (Christina et al., 2023). The second dataset (Root_RC) included observations in ratoon crop of belowground fine (RDMf) and coarse root dry mass (RDMc) measured during a one-year growth with optimal fertilization and irrigation (Versini et al., 2020). Both Root_PC and Root_RC trials were performed in the CIRAD's experimental station of La Mare in Reunion Island. Finally, the third dataset (Root_10sites) included observations in ratoon crop of total root dry mass (RDM) measured at the sugarcane harvest in ten different trials across Reunion Island under fertilized conditions (Chevalier et al., 2023a). In the Root_10sites, part of the trials were irrigated and others were rainfed, depending on their location. The root proportion in dry mass (RProp) was defined as the ratio of RDM to the total dry mass, calculated as the sum of RDM and aboveground dry mass (ADM). In the Root_PC dataset, the RDM was estimated for RProp calculation based on the specific root length used in the STICS model. Additionally, the stool dry mass (PerDM) was measured in the Root_10 sites dataset and represented the perennial organ in the STICS model. The sugarcane variety differed among trials (Table 1). In the ratoon dataset (Root_RC and Root_10sites), to avoid initialization issues on the root compartment in the STICS

model, we run a theoretical plant crop year before simulating the ratoon crop in succession.

### 2.2.2. Aboveground parameterization dataset

The parameterization dataset included trials from the International Consortium for Sugarcane Modelling (ICSM project) carried out in four different countries: France, Zimbabwe, USA, and South Africa (Table 1, Jones et al., 2019, 2021). Each trial consisted of a two-year sugarcane cycle (plant and first ratoon crop) under partial irrigation and optimal fertilization. Depending on the country, experiments were monitored between 2013 and 2017. In each trial, several varieties were compared, but only the data from the Reunion variety R570 were used in this study to be coherent with the belowground dataset available only for varieties from Reunion Island. Collected data during sugarcane growth included: the fraction of intercepted radiation (fiPAR), the leaf area index (LAI, m²·m⁻²), the aboveground fresh (AFM, Mg ha⁻¹) and dry mass (ADM, Mg ha⁻¹), the stalk fresh (SFM, Mg ha⁻¹) and dry mass (SDM, Mg ha⁻¹) and green leaf dry masses (gLDM, Mg ha⁻¹). Measurement methods of these variables and crop management were described in Jones et al. (2019), (2021). Detailed soil measurements, including wilting point, field capacity, as well as soil nutrient analyses, were collected before the trials and available for soil parameterization.

### 2.2.3. Validation dataset

The ECOFI database (Christina et al., 2020a) was used to evaluate the quality of prediction of the sugarcane growth response to climate, soil water capacity, and irrigation management variability. This database included data from sugarcane experiments performed in different countries from 1986 to 2016. For this study, we have selected trials including the R570 variety, as well as other varieties with both ADM and SFM available data coming from different countries (South Africa – F, Brazil – SP and RB, India – CO and NCO, USA – CP, and tropical islands such as Fidji – Q, Mauritius – WI, West Indies – B, and Reunion – R varieties). For R570, 22 trials were selected in Reunion Island, Brazil,

<caption>**Table 1**</caption>

Description of the datasets used for the parameterization and validation of the STICS model. For each dataset, the number of sites and annual cycle were indicated (with plant – P or ratoon – R crop). An annual cycle was defined as the growth period between two harvest dates (approximatively, one year for sugarcane).

<table><thead><tr><th>Group of datasets</th><th>Dataset name</th><th>Location country</th><th>Nb of sites</th><th>Nb of cycle (P; R)</th><th>Variables</th><th>Variety</th><th>Soil*</th><th>Climate<sup>+</sup></th><th>Irrigation & fertilization</th><th>Source</th></tr></thead><tbody><tr><td rowspan="4">Parameterization</td><td>Root_PC</td><td>Reunion Island</td><td>1</td><td>1;0</td><td>ADM, TRL</td><td>R585</td><td>Nitisol</td><td>Am</td><td>Irrigated & optimal fertilization</td><td>(Christina et al., 2023)</td></tr><tr><td>Root_RC</td><td>Reunion Island</td><td>1</td><td>0;1</td><td>ADM, RDMf, RDMc</td><td>R579</td><td>Nitisol</td><td>Am</td><td>Irrigated & optimal fertilization</td><td>(Versini et al., 2020)</td></tr><tr><td rowspan="2">Root_10sites</td><td rowspan="2">Reunion Island</td><td rowspan="2">10</td><td rowspan="2">0;10</td><td>ADM, RDM</td><td>R579, R582, R584, R585, R586</td><td>Nitisol, Cambisol, Andic Cambisol, Non perhydrated Andosol, Perhydrated Andosol</td><td>Aw, Am, Af</td><td>Irrigated or rainfed & optimal fertilization</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>R570</td><td>Nitisol</td><td>Am</td><td>Partially irrigated & optimal fertilization</td><td>(Jones et al., 2021, 2019)</td></tr><tr><td rowspan="10">Validation</td><td rowspan="10">ECOFI</td><td>Reunion Island</td><td>1</td><td>1;1</td><td>fiPAR, LAI, ADM, SDM, gLDM, dLDM,</td><td rowspan="10">R570</td><td>Rhodic cambisol</td><td>Cwa</td><td></td><td></td></tr><tr><td>South Africa</td><td>1</td><td>1;1</td><td>AFM, SFM</td><td>Eutric luvisol</td><td>Bsh</td><td></td><td></td></tr><tr><td>Zimbabwe</td><td>1</td><td>1;1</td><td></td><td>Dystric sapric histosol</td><td>Cfa</td><td></td><td></td></tr><tr><td>USA</td><td>1</td><td>1;1</td><td></td><td>Not available</td><td>Aw, Am, Af</td><td>Partially irrigated or rainfed & optimal fertilization</td><td>(Christina et al., 2020a)</td></tr><tr><td>Reunion Island</td><td>11</td><td>27;25</td><td>fiPAR, LAI, ADM, SDM, gLDM, dLDM,</td><td></td><td></td><td></td><td></td></tr><tr><td>Guadeloupe</td><td>3</td><td>4;0</td><td>AFM, SFM</td><td></td><td></td><td></td><td></td></tr><tr><td>Brazil</td><td>1</td><td>2;0</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mauritius</td><td>1</td><td>4;0</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reunion Island</td><td>7</td><td>85;47</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Guadeloupe</td><td>3</td><td>9;5</td><td></td><td>Others<sup>x</sup> (30 in total)</td><td></td><td></td><td></td><td></td></tr><tr><td>Brazil</td><td>1</td><td>3;9</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<footnote><sup>*</sup> WRB classification (IUSS Working Group WRB, 2022); <sup>+</sup> Köppen climate classification (Peel et al., 2007); Af: Tropical Rainforest, Am: Tropical Monsoon, Aw: Tropical Savannah, Bsh: hot arid steppe, Cwa: Temperate dry winter and hot summer, Cfa: Temperate hot summer without dry season, <sup>x</sup> Varieties: B37172, B80, B82, BBZ92076, C0312, CP76331, F152, F160, H564848, LF669657, M37664, MEX68200, NCO376, NIF5, Q110, R573, R575, R577, R579, R585, R83288, R932022, RB72454, SM8116, SP733108, SP801842, SP832847, WI79460, WI79461, WI81456.</footnote>
Guadeloupe, and Mauritius, including 62 annual cycles (Table 1). The ECOFI database included crop management, irrigation application, soil water capacity (detailed per layers or not), and meteorological data, but no observations on fertilization or soil mineralization potential. Therefore, simulations were performed without nitrogen stress activated in the model. When available, soil water content parameters included field capacity, wilting point, saturation, and bulk density according to the recommendation of the STICS team (Beaudoin et al., 2022). When field capacity and wilting points per layer were not available, an artificial soil with five layers was built with respect to the available soil water capacity and soil depth provided in the ECOFI database. The sugarcane variables used for validation were the same as the ICSM dataset: fiPAR, LAI, AFM, ADM, SFM, SDM, and gLDM, with the addition of dead leaf dry mass (dLDM). The observed variables were not available in all trials, and the number of observations differed depending on the variables (Table 1).

## 2.3. Parameterization and evaluation procedures

The parameterization consisted of choosing the formalisms and corresponding parameters involved in the simulation. Firstly, all formalisms included in the STICS model that do not influence sugarcane growth in the actual field were deactivated. Additionally, phenological stage parameters were defined to avoid flowering.

The parameterization procedure included three steps: 1) determination of parameters from experimental data by direct measurement or observation, 2) determination of parameters from measurements in the scientific literature, and 3) when measurements were not available, estimation of the most sensitive parameters by optimization. Part of the non-sensitive parameters were taken from a previous sugarcane prototype in the STICS model (referred to as STICS v9 in Table 2). Some parameters were taken from related species (giant grasses) when not available for sugarcane in the literature.

We followed a gradual procedure of model parameterization, starting with the belowground developments, followed by the aboveground development (Fig. 1). We started with the belowground biomass, while forcing aboveground development, given that, in STICS, root biomass is expressed as a proportion of the aboveground biomass. This strategy is recommended by the STICS team when parameterizing a new crop (Beaudoin et al., 2022). First, we parameterized the belowground root development (total root length, TRL, fine, RDMf and coarse, RDMc root dry matter, proportion of root relative to total biomass, RProp) in the Root_PC, Root_RC datasets. To accurately parameterize root allocation, the ADM corresponding to each root system was initially forced to match the observed data by using the radiation use efficiency parameters. All the root parameters were obtained from measurements or literature, except for the krepracperm parameter, which was hypothesized to vary among trials (manually optimized in each trial, Table 2). Manual optimization was performed by iteratively adjusting parameters to minimize differences between simulated and observed values, based on visual assessment and statistical indicators. Similarly, the ADM development was forced in the Root_10sites trials, and krepracperm was manually optimized in each trial to represent RDM variability. The krepracperm parameter allowed for representing different allocation patterns between above and root development. This ratio between root and shoot is known to be highly variable among trials, and the environmental effects explaining these variations are still poorly understood. Additionally, the parameters driving the perennial reserve dynamics were parameterized based on Miscanthus or estimated from measurements. One parameter (remobres) was manually optimized (similarly to the root) to represent measured PerDM. In a second step, the aboveground development was parameterized in the ICSM dataset. In these trials, an average root allocation pattern was chosen (average value of optimized krepracperm in the Root_10sites trials, Table 2). Parameters influencing aboveground parameters were estimated from measurements and literature, except for a few that were optimized or fixed based on expertise (Table 2). In

that case, optimization was performed using the javastics platform. As the duration of the LAI development stages also slightly influenced the belowground development in the STICS model, we repeated the procedure once.

Finally, the model was independently validated using the ECOFI dataset. In both parameterization and validation dataset, simulation quality was assessed using the relative root mean square error (rRMSE, Eq.1), the Nash Sutcliffe efficiency (NSE, Eq. 2), the relative mean bias error (rMBE, Eq.3), and the coefficient of determination (R², Eq. 4). To better identify the rRMSE differences between parameterization and validation phases, and between the parameterization variety (R570) and other varieties, a comparison was made between ICSM_R570 and the validation datasets (ECOFI_R570 and ECOFI_Others), and between the two validation subsets (ECOFI_R570 and ECOFI_Others).

$$rRMSE = \frac{\sqrt{\frac{1}{n} \sum_{i=1}^{n} (S_i - O_i)^2}}{\bar{O}} \quad (1)$$

$$NSE = 1 - \frac{\sum_{i=1}^{n} (O_i - S_i)^2}{\sum_{i=1}^{n} (O_i - \bar{O})^2} \quad (2)$$

$$rMBE = \frac{\frac{1}{n} \sum_{i=1}^{n} (S_i - O_i)}{\bar{O}} \quad (3)$$

$$R^2 = \frac{\sum_{i=1}^{n} ((O_i - \bar{O})(S_i - \bar{S}))}{\sqrt{\sum_{i=1}^{n} (O_i - \bar{O})^2} \sqrt{\sum_{i=1}^{n} (O_i - \bar{O})^2}} \quad (4)$$

Where Si and Oi are the simulated and observed values, Ō and S̄ the mean observed and simulated values, respectively, and n the number of observations.

## 2.4. Domain of validity analysis

We assessed the domain of validity of the model parameterization on two variables, the aboveground dry mass (ADM) and the stalk fresh mass (SFM, commonly referred to as yield) at harvest. Even if the belowground development was parameterized in several varieties from Reunion Island for the belowground part, the main variety used for model parameterization was the R570 variety. In the ECOFI validation dataset, we compared the quality of prediction of the R570 variety for final ADM and SFM to the prediction of other varieties from different countries available in the dataset. To this aim, we calculated the rMBE and rRMSE per variety. In addition, we assessed how these two indicators changed with soil and climatic conditions in the ECOFI dataset according to: i) available soil water capacity (ASWC), ii) sum of temperature, iii) potential evapotranspiration, iv) rainfall and irrigation inputs, and v) global radiation. As these soil and climatic variables were correlated among them, we performed a principal component analysis with a varimax rotation method (psych package, Revelle and Revelle, 2015). Then, the Pearson correlations between rMBE or rRMSE and these principal components were tested.

## 2.5. Script and data availability

All analyses were carried out by using R4.2 (R Development Core Team, 2023). Model's inputs and R script used in this study are available in a gitlab repository (https://gitlab.cirad.fr/mathias.christina/script_peerreviewedarticle/-/tree/master/2025_FCR_STICS). The SticsRpacks package was used to generate the model's inputs and run the model (Vezy et al., 2023a).
<table><caption>**Table 2**

Definition and value of the principal parameters used for simulations.</caption><thead><tr><th>Parameters</th><th>Definition</th><th>Units</th><th>Value</th><th>Source</th></tr></thead><tbody><tr><td colspan="5"><strong>Phasic development</strong></td></tr><tr><td>stlevamf</td><td>Thermal time duration between developmental stage lev (plant emergence) and stage amf (maximum acceleration of leaf area index development)</td><td>°Cd</td><td>1000</td><td>STICS v9</td></tr><tr><td>stamflax</td><td>Thermal time duration between developmental stage amf and stage lax (end of emission of leaves)</td><td>°Cd</td><td>6000</td><td>STICS v9</td></tr><tr><td>stlevdrp</td><td>Thermal time duration between developmental stage lev and stage drp (start of grain filling)</td><td>°Cd</td><td>12000</td><td>Maximum value to avoid grain filling (Beaudoin et al., 2022)</td></tr><tr><td colspan="5"><strong>Leaves</strong></td></tr><tr><td>tcmin</td><td>Minimum temperature at which growth ceases</td><td>°C</td><td>10</td><td>(Inman-Bamber, 1994)</td></tr><tr><td>tcmax</td><td>Maximum temperature at which growth ceases</td><td>°C</td><td>40</td><td>STICS v9</td></tr><tr><td>tcxstop</td><td>Temperature beyond which foliar growth stops</td><td>°C</td><td>100</td><td>STICS v9</td></tr><tr><td>vlaimax</td><td>Ulai at the inflection point of the function DELTAI=f(ULAI)</td><td>-</td><td>2.2</td><td>Based on corn (Beaudoin et al., 2022)</td></tr><tr><td>pentlaimax</td><td>Parameter of the logistic curve of LAI growth</td><td>-</td><td>5,5</td><td>Based on corn (Beaudoin et al., 2022)</td></tr><tr><td>udlaimax</td><td>Ulai from which the rate of leaf growth decreases</td><td>-</td><td>3</td><td>STICS v9</td></tr><tr><td>adens</td><td>Interplant competition parameter</td><td>-</td><td>-0.12</td><td>Based on corn (Beaudoin et al., 2022)</td></tr><tr><td>bdens</td><td>Minimal plant density above which interplant competition starts</td><td>-</td><td>5</td><td>Based on corn (Beaudoin et al., 2022)</td></tr><tr><td>parazofmorte</td><td>C/N ratio of dead leaves for a nitrogen nutrition index equal to 1</td><td>-</td><td>100</td><td>(Ferreira et al., 2015)</td></tr><tr><td>dlaimin</td><td>Accelerating parameter for the lai growth rate</td><td>-</td><td>0</td><td>STICS v9</td></tr><tr><td>Dlaimaxbrut<sup>+</sup></td><td>Maximum rate of gross daily increase of LAI</td><td>m<sup>-2</sup> pl<sup>-1</sup> °Cd<sup>-1</sup></td><td>0.001</td><td>Optimization</td></tr><tr><td>Phyllotherme</td><td>Thermal duration between the apparition of two successive leaves on the main stem</td><td>°Cd</td><td>138</td><td>(Jones et al., 2019)</td></tr><tr><td>Hautmax</td><td>Maximum height of crop</td><td>m</td><td>4</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>khaut</td><td>Extinction coefficient connecting LAI to crop height</td><td>-</td><td>0.7</td><td>STICS v9</td></tr><tr><td>durvieF</td><td>Maximum lifespan of an adult leaf</td><td>°Cd</td><td>150</td><td><sup>3</sup>Estimated from Smit and Singels (2006)</td></tr><tr><td colspan="5"><strong>Radiation interception</strong></td></tr><tr><td>extin</td><td>Extinction coefficient of photosynthetic active radiation in the canopy</td><td>-</td><td>0.65</td><td>(Jones et al., 2019)</td></tr><tr><td colspan="5"><strong>Shoot biomass growth</strong></td></tr><tr><td>temin</td><td>Minimum threshold temperature for net photosynthesis=base temperature</td><td>°C</td><td>10</td><td>(Inman-Bamber, 1994)</td></tr><tr><td>temax</td><td>Maximum threshold temperature for net photosynthesis</td><td>°C</td><td>40</td><td>STICS v9</td></tr><tr><td>teopt</td><td>Start of the thermal optimal plateau for net photosynthesis</td><td>°C</td><td>25</td><td>(Watt et al., 2013)</td></tr><tr><td>teoptbis</td><td>End of the thermal optimal plateau for net photosynthesis</td><td>°C</td><td>32</td><td>(Baez-Gonzalez et al., 2017)</td></tr><tr><td>efcrojuv</td><td>Maximum plant radiation use efficiency during juvenile growth</td><td>g DM MJ<sup>-1</sup></td><td>3.5</td><td>(Jones et al., 2019)</td></tr><tr><td>efcroiveg</td><td>Maximum plant radiation use efficiency during vegetative growth</td><td>g DM MJ<sup>-1</sup></td><td>4.3</td><td>(Jones et al., 2019)</td></tr><tr><td>remobres<sup>+</sup></td><td>Fraction of daily remobilisable C reserves</td><td>d<sup>-1</sup></td><td>0.03</td><td>Manual optimization on Root_10sites dataset</td></tr><tr><td colspan="5"><strong>Partitioning of the biomass organs</strong></td></tr><tr><td>slamax</td><td>Maximum SLA (specific leaf area) of green leaves</td><td>-</td><td>120</td><td>(Christina et al., 2020a)</td></tr><tr><td>slamin</td><td>Minimum SLA (specific leaf area) of green leaves</td><td>-</td><td>80</td><td>(Christina et al., 2020a)</td></tr><tr><td>tigefeuil<sup>x</sup></td><td>Ratio stem/leaf</td><td>-</td><td>3</td><td>Fixed</td></tr><tr><td>propresP</td><td>Proportion of biomass perennial reserves that can be remobilised</td><td>-</td><td>0.6</td><td>80 % of propresPN Chevalier et al. (2023b)</td></tr><tr><td>propresPN</td><td>Proportion of nitrogen perennial reserves that can be remobilised</td><td>-</td><td>0.75</td><td><sup>3</sup>Estimated from Chevalier et al. (2023b)</td></tr><tr><td>Efremobil</td><td>Remobilisation efficiency of perennial carbohydrates reserves</td><td>-</td><td>0.05</td><td>Based on Myscanthus (Strullu et al., 2014)</td></tr><tr><td>Propres<sup>x</sup></td><td>Maximal proportion of biomass reserves in living vegetative organs</td><td>-</td><td>0.025</td><td>Fixed</td></tr><tr><td>tauxmortesP</td><td>Mortality rates of perennial organs</td><td>d<sup>-1</sup></td><td>0.001</td><td>Based on Myscanthus (Strullu et al., 2014)</td></tr><tr><td>Parazoper</td><td>C/N ratio of perennial organs when crop NNI = 1</td><td>g g<sup>-1</sup></td><td>75</td><td>(Ramos, 2023)</td></tr><tr><td>ParazoTmorte</td><td>C/N ratio of dead stems for a nitrogen nutrition index equal to 1</td><td>g g<sup>-1</sup></td><td>41</td><td>(Ramos, 2023)</td></tr><tr><td colspan="5"><strong>Roots</strong></td></tr><tr><td>debsenrac</td><td>Thermal time units defining the beginning of root senescence</td><td>°Cd</td><td>6320</td><td>Chevalier et al., n.d, unpublished</td></tr><tr><td>lvfront</td><td>Root density at the root apex</td><td>cm cm<sup>-3</sup></td><td>0.075</td><td>Based on Myscanthus, (Strullu et al., 2014)</td></tr><tr><td>longsperac</td><td>Specific root length</td><td>cm g<sup>-1</sup></td><td>1410</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>kdisrac</td><td>Rate constant defining root length distribution throughout the profile</td><td>cm<sup>-2</sup></td><td>0.0012</td><td>(Christina et al., 2023)</td></tr><tr><td>alloperirac<sup>x</sup></td><td>Allocation rate of the seed reserves (perisperm) to the rootlet growth</td><td>-</td><td>0.05</td><td>Fixed</td></tr><tr><td>repracpermax</td><td>Maximum root biomass relative to total biomass (permanent trophic link)</td><td>-</td><td>0.38</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>repracpermin</td><td>Minimum root biomass relative to total biomass (permanent trophic link)</td><td>-</td><td>0.07</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>krepracperm<sup>+</sup></td><td>Parameter of biomass root partitioning: evolution of the ratio root/total over growth</td><td>-</td><td>2.8*</td><td>Manual optimization</td></tr><tr><td>parazorac</td><td>C/N ratio of roots when crop nitrogen nutrition index= 1</td><td>-</td><td>60</td><td>(Ramos, 2023)</td></tr><tr><td>lvmax</td><td>Maximum root length density in the top soil</td><td>cm cm<sup>-3</sup></td><td>3.2</td><td>(Christina et al., 2023)</td></tr><tr><td>rapdia</td><td>Ratio of coarse roots diameter to fine roots diameter</td><td>-</td><td>2.84</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>RTD</td><td>Root tissue density</td><td>g cm<sup>-3</sup></td><td>0.286</td><td>(Chevalier et al., 2023a)</td></tr><tr><td>Rayon</td><td>Average radius of the roots</td><td>cm</td><td>0.056</td><td>(Chevalier et al., 2023a)</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Parameters</th><th>Definition</th><th>Units</th><th>Value</th><th>Source</th></tr></thead><tbody><tr><td>propracfmax</td><td>Fraction of fine roots emitted in the layer 0–1 cm (in length, maximum value over the root profile)</td><td></td><td>0.97</td><td>(Christina et al., 2023)</td></tr><tr><td>croirac</td><td>Elongation rate of the root apex</td><td>cm °Cd<sup>-1</sup></td><td>0.05</td><td>(Chopart et al., 2010)</td></tr><tr><td><strong>Water</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>h2ofeuilverte</td><td>Water content of green leaves</td><td>g g<sup>-1</sup></td><td>0.7</td><td>ICSM measurements</td></tr><tr><td>h2ofeuiljaune</td><td>Water content of yellow leaves</td><td>g g<sup>-1</sup></td><td>0.38</td><td>ICSM measurements</td></tr><tr><td>h2otigestruc</td><td>Water content of structural stem part</td><td>g g<sup>-1</sup></td><td>0.75</td><td>ICSM measurements</td></tr><tr><td>h2oreserve<sup>x</sup></td><td>Water content of crop reserve</td><td>g g<sup>-1</sup></td><td>0.7</td><td>Fixed</td></tr><tr><td>kmax</td><td>Crop coefficient</td><td></td><td>1</td><td>STICS v9</td></tr><tr><td>psisto</td><td>Potential of stomatal closing</td><td>Bars</td><td>25</td><td>Estimated from (Smit and Singels, 2006)</td></tr><tr><td>psiturg</td><td>Potential of the beginning of decrease of the cellular extension</td><td>Bars</td><td>15</td><td>Estimated from (Smit and Singels, 2006)</td></tr><tr><td>rapsenturg</td><td>Threshold soil water content active to simulate water senescence stress</td><td></td><td>0.1</td><td>STICS v9</td></tr><tr><td>swfacmin</td><td>Minimal value for drought stress indices</td><td></td><td>0.1</td><td>STICS v9</td></tr><tr><td><strong>Nitrogen</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>vmax1</td><td>Maximum specific N uptake rate with the low affinity transport system</td><td>µmole cm<sup>-1</sup> h<sup>-1</sup></td><td>0.0018</td><td>STICS v9</td></tr><tr><td>kmabs1</td><td>Affinity constant of N uptake by roots for the fast uptake system</td><td>µmole L<sup>-1</sup></td><td>50</td><td>STICS v9</td></tr><tr><td>vmax2</td><td>Maximum specific N uptake rate with the high affinity transport system</td><td>µmole cm<sup>-1</sup> h<sup>-1</sup></td><td>0.017</td><td>STICS v9</td></tr><tr><td>kmabs2</td><td>Affinity constant of N uptake by roots for the low uptake system</td><td>µmole L<sup>-1</sup></td><td>25000</td><td>STICS v9</td></tr><tr><td>adil</td><td>Parameter of the critical dilution curve [Nplante]=adilMS<sup>-</sup>(bdil)</td><td>%</td><td>2.4072477</td><td>(Pouzet et al., 1999)</td></tr><tr><td>bdil</td><td>Parameter of the critical dilution curve [Nplante]=adilMS<sup>-</sup>(bdil)</td><td>%</td><td>0.4182679</td><td>(Pouzet et al., 1999)</td></tr><tr><td>bdilmax</td><td>Parameter of the maximum dilution curve [Nplante]=adilmax MS<sup>-</sup>(bdilmax)</td><td>%</td><td>0.4182679</td><td>assumed = bdil</td></tr><tr><td>adilmax</td><td>Parameter of the maximum dilution curve [Nplante]=adilmax MS<sup>-</sup>(bdilmax)</td><td>%</td><td>2.4072477</td><td>assumed = adil</td></tr><tr><td>masecNmax</td><td>Aerial biomass above which N dilution occurs (critical and maximal curves)</td><td>Mg ha<sup>-1</sup></td><td>1</td><td>STICS v9</td></tr><tr><td>INNimin</td><td>INNI (instantaneous NNI) corresponding to INNmin</td><td></td><td>0</td><td>(Beaudoin et al., 2022)</td></tr></tbody></table>

STICS V9 refers to the parameters of the sugarcane prototype in the STICS model. ICSM is the International Consortium for Sugarcane Modelling dataset, + indicated the optimized parameters, * the mean value of the optimized parameters across the 10 sites, ^ the fixed parameters, § Estimation methods: durieF was estimated as the inverse of leaf senescence rate under non-limiting soil water conditions (Fig. 4 from Smit and Singels, 2006); propresPN was estimated based on ¹⁵N measurements during N reallocation to the aboveground parts after harvest in a pot experiment (Chevalier et al., 2023b); psisto was estimated as the leaf water potential corresponding to zero stomatal conductance (Fig. 9 from Smit and Singels, 2006); psiturg was fixed at 15 kPa, assuming a leaf area reduction rate that increased below a relative soil water content of 0.4 (Fig. 6 and Fig. 7 from Smit and Singels, 2006).

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Schematic representation of the parameterization and evaluation procedure in the sugarcane STICS model, along with the names of parameters considered (details in Table 2). Parameter values were determined from experimental data, literature, or by optimization (parameters in bold font). Note that in the parameterization of root and perennial reserve, the ADM values corresponding to the root or perennial reserve data used were initially forced to fit the observed measurements.</figcaption></figure>
# 3. Results

## 3.1. Belowground and perennial sugarcane parameterization with the root dataset

Following the forcing of aboveground dry mass (ADM), which enabled a good simulation relative to the observations (Fig. 2a, b, c), the parameterization of the root allowed for an accurate simulation of total root length (TRL, Fig. 2d), root dry mass differentiated between coarse and fine roots (RDM, Fig. 2e), and the proportion of roots relative to total dry mass (RProp, Fig. 2g, h), with rRMSE values ranging from 0.16 to 0.43. Based on the evaluation using the Root_10sites dataset, the model also demonstrated good performance for RDM (Fig. 2f), with a low rRMSE value of 0.10 and a high NSE value (0.94, Table 3). This result illustrates the ability of the STICs model to capture the variability of sugarcane root biomass across multiple environments, which is a relevant point, given the important part it holds. The parameterization of the perennial reserve showed a lower model efficiency (0.41) compared to other variables (Fig. 2i, Table 3), which is not critical, as

the related parameters were set to allow the model to simulate values closer to our observations. The model efficiencies for total root length and fine root dry matter were negative, indicating the model's inability to accurately predict these belowground variables.

## 3.2. Aboveground sugarcane parameterization with the ICSM dataset

The outputs of the STICS model following parameterization for sugarcane growth using data from four different countries and two successive crop cycles (one planting and one ratooning) are presented in Fig. 3. The observations for each variable ranged from 24 to 37 (Table 3). Overall, the model appears to accurately represent the variations over the growing periods (either in planting or ratooning) of leaf area index (Fig. 3a, b), the fraction of intercepted radiation (Fig. 3c, d), the aboveground and stalk fresh mass (Fig. 3e, f), and the aboveground and stalk dry mass (Fig. 3g, h). However, there were differences in model prediction accuracy among the observed variables while considering the statistical indicators (Table 3). Aboveground, stalk fresh and dry matter displayed a relative rRMSE oscillating between 0.24 à 0.38 with good

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Predicted (line) and observed (dots) belowground variables in the Root_PC and Root_RC parameterization datasets during the crop cycle (a, b, d, e, g, h), and comparison between predicted and observed variables in the Root_10sites parameterization dataset (c, f, i). In c, f, and i, a linear regression was added between predicted and observed variables (blue line). Variables included aboveground dry mass (ADM), total root length (TRL), dry mass (RDM), root proportion in total dry mass (RProp), and perennial dry mass (PerDM). Statistical indices associated with this dataset are described in Table 3. For note, the ADM was forced in this parameterization dataset.</figcaption></figure>
<caption>**Table 3**</caption>

Statistical indices between predicted and observed variables in the different parameterization and validation datasets: number of observations (n), rRMSE, NSE, R², and rMBE. The variety in each dataset are indicated with Rx indicating different varieties from Reunion Island. Variables include total root length (TRL), fine (RMDF) and coarse root dry mass (RMDc), root proportion in total dry mass (RProp), perennial dry mass (PerDM), leaf area index (LAI), fraction of intercepted radiation (fiPAR), aboveground fresh (AFM) and dry mass (ADM), stalk fresh (AFM) and dry mass (SDM), green (gLDM) and dead leaf dry mass (dLDM).

<table><thead><tr><th>Group of datasets</th><th>Dataset (Variety)</th><th>Variables</th><th>n</th><th>rRMSE</th><th>NSE</th><th>R<sup>2</sup></th><th>rMBE</th></tr></thead><tbody><tr><td rowspan="10">Parameterization</td><td>Root_PC<br>(R585)</td><td>TRL</td><td>4</td><td>0.32</td><td>&lt; 0</td><td>0.62</td><td>0.21</td></tr><tr><td>Root_RC<br>(R579)</td><td>RProp</td><td>4</td><td>0.34</td><td>0.17</td><td>0.96</td><td>-0.27</td></tr><tr><td rowspan="3">Root_10sites<br>(Rx)</td><td>RDMf</td><td>4</td><td>0.21</td><td>&lt; 0</td><td>0.45</td><td>0.02</td></tr><tr><td>RDMc</td><td>4</td><td>0.43</td><td>0.02</td><td>0.50</td><td>-0.07</td></tr><tr><td>RProp</td><td>4</td><td>0.16</td><td>0.90</td><td>0.92</td><td>-0.07</td></tr><tr><td rowspan="3">ICSM<br>(R570)</td><td>RDM</td><td>10</td><td>0.10</td><td>0.94</td><td>0.95</td><td>-0.02</td></tr><tr><td>RProp</td><td>10</td><td>0.25</td><td>0.82</td><td>0.82</td><td>0.01</td></tr><tr><td>PerDM</td><td>10</td><td>0.39</td><td>0.41</td><td>0.47</td><td>0.06</td></tr><tr><td rowspan="10">Validation</td><td rowspan="5">ECOFI<br>(R570)</td><td>LAI</td><td>24</td><td>0.37</td><td>0.45</td><td>0.49</td><td>0.07</td></tr><tr><td>fiPAR</td><td>37</td><td>0.14</td><td>0.85</td><td>0.86</td><td>-0.03</td></tr><tr><td>ADM</td><td>32</td><td>0.28</td><td>0.88</td><td>0.89</td><td>0.10</td></tr><tr><td>SDM</td><td>32</td><td>0.38</td><td>0.83</td><td>0.91</td><td>0.26</td></tr><tr><td>gLDM</td><td>32</td><td>0.52</td><td>&lt; 0</td><td>0.35</td><td>-0.33</td></tr><tr><td rowspan="5">ECOFI<br>(others)</td><td>AFM</td><td>32</td><td>0.26</td><td>0.83</td><td>0.85</td><td>-0.01</td></tr><tr><td>SFM</td><td>24</td><td>0.24</td><td>0.78</td><td>0.79</td><td>0.04</td></tr><tr><td>LAI</td><td>172</td><td>0.43</td><td>0.49</td><td>0.58</td><td>-0.18</td></tr><tr><td>fiPAR</td><td>65</td><td>0.32</td><td>0.67</td><td>0.71</td><td>0.10</td></tr><tr><td>ADM</td><td>230</td><td>0.49</td><td>0.74</td><td>0.75</td><td>0.07</td></tr><tr><td rowspan="10">Validation</td><td rowspan="5">ECOFI<br>(R570)</td><td>SDM</td><td>119</td><td>0.55</td><td>0.68</td><td>0.69</td><td>0.10</td></tr><tr><td>gLDM</td><td>100</td><td>0.50</td><td>&lt; 0</td><td>0.47</td><td>-0.36</td></tr><tr><td>dLDM</td><td>23</td><td>0.40</td><td>0.87</td><td>0.93</td><td>0.17</td></tr><tr><td>AFM</td><td>121</td><td>0.49</td><td>0.72</td><td>0.85</td><td>0.26</td></tr><tr><td>SFM</td><td>109</td><td>0.49</td><td>0.66</td><td>0.67</td><td>0.06</td></tr><tr><td rowspan="5">ECOFI<br>(others)</td><td>LAI</td><td>178</td><td>0.46</td><td>0.54</td><td>0.54</td><td>-0.03</td></tr><tr><td>fiPAR</td><td>928</td><td>0.43</td><td>0.50</td><td>0.64</td><td>0.22</td></tr><tr><td>ADM</td><td>893</td><td>0.46</td><td>0.73</td><td>0.76</td><td>0.10</td></tr><tr><td>SDM</td><td>527</td><td>0.50</td><td>0.70</td><td>0.74</td><td>0.16</td></tr><tr><td>gLDM</td><td>307</td><td>0.57</td><td>0.21</td><td>0.47</td><td>-0.30</td></tr><tr><td rowspan="5">Validation</td><td rowspan="5">ECOFI<br>(R570)</td><td>dLDM</td><td>253</td><td>0.96</td><td>0.22</td><td>0.75</td><td>0.52</td></tr><tr><td>AFM</td><td>701</td><td>0.52</td><td>0.53</td><td>0.65</td><td>0.10</td></tr><tr><td>SFM</td><td>498</td><td>0.50</td><td>0.64</td><td>0.66</td><td>0.10</td></tr></tbody></table>

NSE varying between 0.78 and 0.88. In addition, the observed and simulated values were strongly correlated, with all R² values above 0.80. On the contrary, the LAI showed lower rRMSE, NSE, and R² compared to biomass variables. Nonetheless, the fiPAR showed accuracy similar to biomass variables, which indicated that the lower accuracy in the simulation of LAI was not too impactful on the quality of the biomass simulation. Finally, the model was not able to accurately simulate green leaf dry mass, as indicated by the negative model efficiency (Table 3).

### 3.3. Validation of the STICS model in the ECOFI dataset

The evaluation of the STICS model used 2–28 times more observations than the parameterization, depending on the specific variable considered (Table 3). The comparison between observations and STICS model simulations for sugarcane variables, including LAI (Fig. 4a), fiPAR (Fig. 4b), AFM (Fig. 4c), SFM (Fig. 4d), ADM (Fig. 4e), and SDM (Fig. 4f), were shown in Fig. 4. Overall, the STICS model demonstrated satisfactory ability to predict fiPAR, ADM, SDM, AFM, and SFM across different sugarcane varieties, although the statistical indicators showed less accuracy compared to the parameterization phase (Fig. S1a). The aboveground and stalk fresh/dry biomass for the variety mainly used in parameterization (R570) were simulated with rRMSE values ranging from 0.49 to 0.55, which were higher than the errors recorded during parameterization. Model efficiency also decreased compared to parameterization, with values between 0.66 and 0.74, but the performance remained relatively strong, demonstrating the genericity and robustness of the model. The model performance for these biomass variables remained consistent across all other sugarcane varieties evaluated, with rRMSE values between 0.46 and 0.52, and NSE values ranging from 0.53 to 0.73 (Table 3). Similarly to biomass, fiPAR showed rRMSE values of 0.32 and an NSE value of 0.67 for R570, though the accuracy was lower for other varieties. Among the evaluated variables, dry leaf mass, which

was not included in the parameterization phase, showed a good performance for R570 but not for other varieties. LAI and green leaf dry mass (gLDM) followed the same trend as in parameterization, revealing some model limitations in simulating these variables accurately. The model efficiencies for these two variables, not exceeding 0.54 for LAI and 0.21 for gLDM, were the lowest compared to other variables. Additionally, the model bias was negative for both variables, indicating an underestimation by the model. It is noteworthy that gLDM prediction was strongly underestimated (bias between -0.36 and -0.33) in all the validation and parameterization phases, and the model efficiency for this variable using the ECOFI_R570 dataset was negative. For all variables assessed during the validation phase, the model produced some stagnant simulation outputs, while the corresponding field measurements showed variation, mainly due to comparisons of different varieties in the same trials (up to 20 varieties in some trials, Fig. 4).

### 3.4. The accuracy of the model is depending on variety parameterization

The model displayed various bias errors in the sugarcane's above-ground dry mass (ADM) (Fig. 5a) and stalk fresh mass (SFM) at harvest (Fig. 5b), depending on the variety. In total, the errors of 30 sugarcane varieties were compared with the R570 variety. The number of cycles per variety varied significantly, ranging from 2 to 45 for ADM and 2–32 for SFM, with R570 having the highest number of points (Fig. 5a, b). For ADM at harvest, the model overall led to low rMBE for most varieties. The model bias was positive for most varieties (19 out of 31), unlike R570 and 11 other varieties, which showed negative values. This suggests that the model tended to overestimate the ADM for many sugarcane varieties. For most varieties, including R570, the model bias remained low, not exceeding ±0.2, indicating overall good model performance. However, three varieties (M37664, Q110, and RB72454) showed the highest biases, close to or exceeding ±0.5. Regarding SFM,
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparison between predicted and observed variables in the ICSM parameterization dataset (a, c, e, g), and dynamics of predicted (lines) and observed (dots) variables during the crop cycle. Simulations were performed in Reunion Island (RUN), Zimbabwe (ZIM), South Africa (SAF), and United States of America (USA) in plant (P0) and ratoon crops (R1). Variables included leaf area index (LAI), fraction of intercepted radiation (fiPAR), aboveground dry (ADM) and fresh mass (AFM), stalk dry (SDM) and fresh mass (SFM), and green leaf dry mass (gLDM). Statistical indices associated with this dataset are described in Table 3.</figcaption></figure>

the bias indicated that the model could have low errors for most varieties. A similar trend of positive bias was observed for many varieties, including R570. Most varieties with a negative bias for ADM displayed a similar trend for SFM. The bias values did not exceed ±0.3 for most varieties, following the same pattern as ADM, though the Q110 variety exhibited the highest bias at 0.8. The model appeared to strongly overestimate SFM for this variety, which indicates that specific parameters would be required to simulate this variety better. The rRMSE on ADM and SFM values was below 0.4 for most varieties (Fig. S2), but as with rMBE, the highest rRMSE values were observed for SFM. The rRMSE values observed between ECOFI_R570 (parameterization variety) and ECOFI_Others (international varieties) datasets used for the validation were quite similar, except for dLDM (Fig S1b).

### 3.5. Model's error at harvest depending on soil and climate

Based on a principal component analysis, the soil and climatic variables were summarized into three principal components PC1, PC2, and PC3, representing 37, 36, and 22 % of variable variance in the ECOFI dataset (Fig. 6a). PC1 was mainly correlated with the sum of temperature (sTmean) and global radiation (sRg) during the crop cycle. PC2 was mainly correlated with the available soil water capacity (ASWC) and the total rainfall and irrigation during the crop cycle (RainIrrig). PC3 was mainly correlated with the sum of potential evapotranspiration (sETP).

The rMBE on ADM at harvest was positively correlated with the PC1 (p = 0.045) and more strongly with PC2 (p = 0.001), but not with PC3 (p = 0.102, Fig. 6b). On the contrary, rMBE on SFM at harvest was not correlated to neither PC1 (p = 0.195), PC2 (p = 0.733) nor PC3 (P = 0.993). The correlation between rMBE on ADM and PC1 was mainly due to negative values of rMBE for low PC1 values (Fig. 6c). The rMBE on ADM also increased with PC2 with negative bias at low ASWC and RainIrrig, and positive bias for high ASWC and RainIrrig (Fig. 6d). Similar to rMBE on ADM, a negative correlation was observed between rRMSE on ADM and PC1 (p = 0.081) or PC2 (p = 0.084) only, but with a p < 0.1 (Fig. S3). Likewise, no correlation was observed with rRMSE on SFM.

## 4. Discussion

### 4.1. Variability of the data used and specificity of the parameterization approach

The data used for the parameterization and validation of the model in this study are diverse, encompassing a variety of sugarcane types, soil, rainfall, and temperature, which is consistent with prior research (Colmanetti et al., 2024; Keating et al., 1999; Peng et al., 2020). However, many studies have utilized less variable datasets in terms of the experimental sites and sugarcane varieties used for this procedure. With
<figure><img src="image_7.png" /><figcaption>Fig. 4. Comparison between predicted and observed variables in the ECOFI validation dataset. Blue dot indicated the R570 variety and red dot indicated other varieties presented in the dataset. Linear regressions were added between predicted and observed variables. Variables included leaf area index (LAI; a), fraction of intercepted radiation (fiPAR; b), aboveground fresh (AFM; c) and dry mass (ADM; d), stalk fresh (SFM; e) and dry mass (SDM; f). Statistical indices associated with this dataset are described in Table 3. Note that the ECOFI dataset included varietal trials with up to 20 varieties compared in the same trial.</figcaption></figure>

the exception of Colmanetti et al. (2024), which employed data from 1759 different sites and more than 40 varieties within the same country (Brazil), most studies were limited to fewer than 10 experimental sites and a smaller number of varieties (Dias et al., 2021, 2019; Jones and Singels, 2018; Peng et al., 2020; Roetter et al., 2018; Singels et al.,

2014). Notably, our study utilized more than 31 sugarcane varieties, and data from seven countries with diverse soil types and climates, which allowed for a more robust validation of the model and also to evaluate the accuracy of simulations when no specific parameterization for varieties is done in the case of sugarcane.
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Relative mean bias error (rMBE) on aboveground dry mass (ADM, a) and stalk fresh mass (SFM, b) at harvest depending on varieties in the ECOFI validation dataset. The R570 variety is indicated in blue and other varieties in red. The number of crop cycles available for each variety are indicated above the bars. The dashed lines indicate a rMBE value of ±0.3.</figcaption></figure>

Our parameterization procedure involved an extensive exploration of root dynamics and preliminary investigations into perennial reserves for sugarcane's crop. This is relatively novel, as most sugarcane modeling studies have not evaluated this crucial pool. While, for some varieties, perennial reserves might initially appear to have a minor effect on regrowth and soil N (from decomposition) due to their low biomass, typically around 1–3 Mg ha⁻¹ (Fig. 2b), the focus on root dynamics is particularly important when simulating sugarcane over longer periods. Root biomass can represent up to 25 % of the total plant biomass at harvest in certain cases (Fig. 2h), and its decomposition over time could significantly contribute to nitrogen availability and carbon storage in the soil, thereby influencing crop growth and development (Poultney et al., 2024).

The approach used in our study is comparable to some of the calibration steps defined in the protocol developed by Wallach et al. (2024), (2023) as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Notably, our procedure included selecting default parameters, listing observed variables alongside their corresponding simulated variables, defining groups of variables, and determining the order of calibration (from belowground to aboveground variables in our case). We also estimated parameters that could influence these variables. However, unlike the protocol, we did not apply the

AIC (Akaike Information Criterion) approach to select which parameters to estimate. Instead, our choices were based on expert knowledge of the model and prior sensitivity analysis, which had already identified the most sensitive parameters. It is important to note, however, that the AIC method is generally more robust in avoiding over-parameterization, a significant risk given the large number of parameters that could potentially be estimated in soil-crop models. In our case, despite not using the AIC, we limited the number of estimated parameters to just three, with the remaining values sourced from literature or direct measurements.

## 4.2. STICS model's ability to simulate sugarcane growth under various climates

The parameterization of the perennial reserve in our model yielded statistical indicator values comparable to those reported by Strullu et al. (2020) for alfalfa, another perennial crop. Specifically, we obtained a rRMSE of 0.39 compared to their 0.19, a NSE of 0.41 compared to their 0.47, and a coefficient of determination (R²) of 0.47 against their 0.62. These results suggest that, while there are slight differences, the model provides a reasonably accurate representation of the dynamic simulation of the perennial reserve in sugarcane, similar to the performance
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Change in relative mean bias error (rMBE) on aboveground dry mass (ADM) and stalk fresh mass (SFM) at harvest with soil and climatic components in the ECOFI dataset. (a) represents the correlation between soil and climatic variables with the three principal components (PC1, PC2, and PC3). (b) represents the correlation between rMBE and principal components. (c) and (d) represent the change in rMBE on ADM with PC1 and PC2 values. Soil and climatic variables included available soil water capacity (ASWC), sum of daily temperature (sTmean), evapotranspiration (sETP), global radiation (sRg), and total rainfall and irrigation during the crop cycle.</figcaption></figure>

observed in the alfalfa crop study. However, further studies could be conducted to enhance the representation of this pool, relying on more robust additional field data for parameter calculation or estimation.

The STICS model successfully simulated the sugarcane aboveground biomass with statistical indicators comparable to those obtained in previous studies for other crops using the same model. In comparison, the study by Falconnier et al. (2019) on Faba bean using the STICS model achieved a NSE of 0.70 for aboveground dry mass, which is close to the NSE values obtained in our study for sugarcane varieties R570 (0.73) and others (0.74). However, their study reported lower rRMSE values, ranging from 0.21 to 0.31, whereas our study found rRMSE values between 0.42 and 0.55. Similarly, Strullu et al. (2020) reported NSE, rRMSE, and R² values of 0.70, 0.36, and 0.73, respectively, for the alfalfa crop. Our results showed stronger performance than those reported by Sow et al. (2024) for millet, where the STICS model produced negative NSE values and rRMSE values exceeding 0.50 for total biomass and leaf area index. The model's inability to predict TRL (Total Root Length) and gLDM (green Leaf Dry Mass) did not affect the simulation of other variables, suggesting that these components are weakly connected with the rest of the system in the model structure. This could also be explained by the fact that the conditions were not limiting enough to reveal the effect of TRL. Relative to other sugarcane models, the simulations obtained with the STICS model were quite comparable or better. For example, the recently developed ECOSMOS model successfully simulated key variables such as stalk dry mass (SDM), green leaf dry mass (gLDM), leaf area index (LAI), and fraction of intercepted photosynthetically active radiation (fiPAR) during the parameterization phase, with statistical indicators close to ours (Colmanetti et al., 2024). The NSE values ranged from 0.86 to 0.89 for SDM, 0.58–0.65 for gLDM, 0.62–0.67 for LAI, and 0.72–0.89 for fiPAR. Similarly, the rRMSE varied

from 0.24 to 0.46 for SDM, 0.30–0.44 for gLDM, 0.26–0.45 for LAI, and 0.11–0.23 for fiPAR, depending on the experimental sites considered in Brazil (Colmanetti et al., 2024). In another study, Jones et al. (2021) evaluated three sugarcane models, DSSAT-CANEGRO, APSIM, and MOSICAS, and found R² values ranging from 0.24 to 0.35 for LAI, 0.65–0.86 for fiPAR, 0.82–0.90 for aboveground dry mass (ADM), and 0.84–0.88 for SDM. These align well with our findings, even though the database used was much smaller than in this study. Additionally, Dias and Sentelhas (2017) assessed the DSSAT-CANEGRO and APSIM models and found a NSE value of 0.50 for stalk fresh mass, which was lower than our results. However, the FAO_AZM model yielded a NSE value of 0.64 during the evaluation phase, similar to our findings. Overall, the models' performance in simulating gLDM and LAI tended to be weaker across these above studies, in agreement with our observations.

The parameterization phase generally showed better statistical indicators than the validation phase, which is consistent with using data from a single variety for parameters settings during parameterization of the aboveground variables versus data from the larger set of varieties in the validation phase. Nevertheless, even without specific parameterization for each variety, the STICS model produced accurate simulations, suggesting that its performance could be further improved with variety-specific parameterization. In the validation phase, the stagnant simulation values observed for different variables may be primarily due to different sugarcane varieties compared in the same trials (up to 20 varieties in some trials). Nonetheless, these stagnant simulation values could also result from the deactivation of nitrogen stress in the validation phase, due to limited soil data, which may not have been fully justified in some trials. Additionally, other stresses related to P and K, which are not accounted for by the model, could have contributed to the discrepancies between observations and simulations. These results
indicate that the model may not effectively capture the variation in data from the same trial involving different varieties, whose responses to stress could vary significantly.

### 4.3. Limitations of the STICS model in simulating sugarcane growth

Although the STICS model showed a solid ability to simulate aboveground dry mass (ADM) and stalk fresh mass (SFM) in sugarcane, significant biases were observed for some varieties, suggesting limitations in handling variety-specific traits. The large bias observed in aboveground biomass at harvest for certain varieties (compared with R570) suggested that some parameters could be adjusted to represent different behaviors according to traits for development, growth rate, or sensitivity to water and N stresses. The first type of parameters that could differ between varieties is those controlling the maximum radiation use efficiency (RUE), as highlighted by Jones et al. (2019). Nevertheless, work in progress within the framework of the “International Consortium for Sugarcane Modeling” suggests that in most cases, elite varieties should present similar maximum RUE but could highly differ in terms of actual RUE (calculated over the entire crop cycle). Consequently, it is rather the parameters affecting sensitivity to water stress that could vary greatly between varieties. Indeed, it is known that sugarcane varieties can differ greatly in their sensitivity to water stress (Venkataramana et al., 1986). Additionally, the larger bias in the simulation of SFM, compared to ADM for some varieties, indicates that the model may require a more relevant approach to simulate this variable such as the equations, and the parameters “tigefeuil” (controlling allocation between stem and leaves) or “h2otigestruc” (controlling stem water content) are difficult to parameterize. In our study, these parameters were fixed and assumed to be similar across all varieties, while they might be variety-specific within a reasonable range (Christina et al., 2020a). The strong underestimation of gLDM by the model suggests that improvements could be made either in the parameterization process or in the equations governing this variable.

The STICS model currently cannot simulate sugar accumulation in the stem, but in the fruit or grain, which limits its applicability for sugar yield estimation. This functionality is implemented in other models such as DSSAT-CANEGRO and APSIM-Sugar (Marin et al., 2015). Sugar accumulation could be incorporated into STICS by introducing a formalism that divides the daily biomass allocated to the stem into structural and sucrose pools, once a minimum stem biomass is reached. The rate of biomass partitioning and division would be influenced by nitrogen availability, temperature, and water stress. Some previous studies with the STICS model attempted to simulate sugar accumulation in the stem by considering the stem as a fruit. However, this approach yielded satisfactory sugar content results, but negatively affected the accuracy of stalk fresh yield simulations (Christina et al., 2020b). In our study, we chose not to follow this method to simulate better yield, which is essential information for farmers and sugar industries.

Overall, the STICS model’s simulations of ADM exhibited variable relative bias errors based on climatic conditions. In optimal growth conditions, characterized by high rainfall and temperatures, the model tended to show a positive bias, meaning it overestimated ADM. This overestimation could be linked to the “reduced growth phenomenon”, where sugarcane yield declines with plant age, even in favorable conditions, due to a decline in radiation use efficiency (Dias et al., 2019; Park et al., 2005). This phenomenon is currently not accounted for by the model, potentially contributing to its overestimation. The overestimation could also be linked to biotic stress (pests and weeds) not accounted for by the model. However, this is likely limited in our case, as the trials were conducted at experimental stations with chemical weed control. Conversely, in less optimal conditions, such as periods with low temperatures, reduced global radiation, and insufficient rainfall, the model demonstrated a negative bias, indicating it underestimated ADM. This underestimation suggests that the model may overestimate the impact of environmental stressors on sugarcane growth, or that the soil

explored by sugarcane roots is deeper than generally considered, and then for the soil parameterization. Fine-tuning the representation of different stress factors (e.g., water, temperature, and radiation stress) could help improve the model’s accuracy in suboptimal environments. Additionally, this raises the importance of better simulating root growth, which could enable more precise water stress simulations that require to know the soil characteristics better.

We successfully simulated root growth in sugarcane using the STICS model, but only by estimating the root biomass partitioning parameter (krepracperm) specific to each site. This highlights a key limitation of the model regarding root growth simulation. The need to parameterize this parameter for each location suggests that STICS struggles with simulating root-to-shoot biomass partitioning in a generalized manner. In addition, the root data in this study were collected down to a depth of 1.2 m, although roots could extend deeper than 4 m (Battle Laclau and Laclau, 2009; Smith et al., 2005), suggesting that more extensive and variable root data could be needed for a more robust parameterization. Nonetheless, approximately 85 % of the root biomass typically occurs in the top 60 cm (Smith et al., 2005). Future studies should focus on understanding the environmental factors that influence the root-to-shoot ratio, which, once incorporated in the model, would improve the simulation of root dynamics across various conditions.

## Funding

The research was supported by the CIRAD, France, the Conseil Regional de La Reunion, France, the French Ministry of Agriculture and Food, France, and the European Union as part of the CAPTERRE project (Feader - REU77071-1-000010 and Feder-2024-1248-005756 programs).

## CRediT authorship contribution statement

**Mathias Christina:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Jean-Christophe Soulie:** Writing – review & editing, Methodology, Investigation, Funding acquisition, Conceptualization. **Marion Ramos:** Writing – review & editing, Investigation, Conceptualization. **Léa Chevalier:** Writing – review & editing, Investigation, Conceptualization. **Maxime Chaput:** Methodology, Investigation, Conceptualization. **Lelenda Florent Kebalo:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. **Antoine Versini:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antoine Versini Mathias Christina Jean-Christophe Soulie reports financial support was provided by CAPTERRE project. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We would like to thank the International Consortium for Sugarcane Modelling (ICSM) and its constituent organizations for sharing the data.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.fcr.2025.110100.
# Data availability

Data will be made available on request.

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