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# Revisiting the dynamic simulation model of land-use changes in the Sudano-sahelian countries of Africa (SALU): An open-access application to Senegal

Anem Dupré<sup>ID*</sup>, Paul-Alain Raynal<sup>ID</sup>, Isabelle Gounand<sup>ID</sup>, Caroline Pierre<sup>ID</sup>

Sorbonne Université, UPEC, CNRS, IRD, INRA, Institut d'Ecologie et des Sciences de l'Environnement, iEES-Paris, 4 Place Jussieu, 75005, Paris, France

## ARTICLE INFO

**Keywords:**

Intensification

Sahel

Agriculture

Cropland

Processed-based model

Natural resources

## ABSTRACT

In the Sahel, the sustainability of human land-use is threatened by climate change and demographic pressure. Research is urgently needed to provide governments with relevant leads to prevent land degradation and ensure the population access to resources such as food and fuelwood. Little observation data is available, and canonical land-use models have difficulty representing Sahelian dynamics. One of the few models developed to fit characteristics of the region is the dynamic simulation model of land-use changes in the Sudano-Sahelian countries of Africa (SALU) developed by Stephenne and Lambin (2001). This study aims at adapting this model to the current state of the art, providing explicit calculation steps, to reconstruct past land-use dynamics in Senegal from 1961 to 2020. An extensive bibliographic search was used to obtain parameter value ranges and the model's sensitivity to parameter uncertainties was assessed through δ-indices calculation. Applying the model at national scale predicted trends coherent with available literature, with agricultural expansion leading to deforestation, and a switch to intensification in the mid-1990s affecting both livestock forage consumption and fallowing time. Calculating land-demands in the Groundnut basin, a subregion of Senegal, showed the limits of the model when downscaling, with current hypotheses insufficient to reconstruct land-uses in the region when faced with demands too high to be satisfied by the local production. This work opens perspectives to refine land-use modeling in the Sahel, including prospective scenarios.

## 1. Introduction

In the last decades, the increase in anthropogenic pressure on land and resources has been accompanied by major land-use changes all over the world. The growing human population drove the demand for food, shelter and freshwater, hence the expansion of agricultural and urbanized land (Winkler et al., 2021). At the global scale, croplands are estimated to expand from 2.7 to 4.9 Mha each year between 2000 and 2030 (Lambin and Meyfroidt, 2011). From 1980 to 2000, half of the expansion of agricultural lands in the tropics came at the expanse of forests, causing the loss of the native habitats of multiple species (Lambin and Meyfroidt, 2011). These major changes in land-use and cover are responsible for the emission of a quarter of global greenhouse gases emissions, in particular through the loss of sinks for atmospheric carbon, but also methane and nitrous oxide emissions due to agricultural activities (Lambin and Meyfroidt, 2011; Tubiello et al., 2015). Through GHG emissions, nitrogen leaching and overall land degradation (Bodirsky et al., 2014; Foley et al., 2005), changes in land-use can thus greatly affect the land-based ecosystem services on which they depend, jeopardizing their sustainability.

This issue is particularly acute in the Sahel, the semi-arid region located south of the Saharan desert and defined by a north-south gradient of annual precipitation ranging from 200 to 600 mm (Lebel and Ali, 2009). Sahelian governments face the challenge of sustainably meeting the needs of one of the fastest growing population in the world, in a region characterized by economic poverty and low agricultural productivity (van Vliet et al., 2013; Oxford Analytica, 2021). Since the 1960s, changes in climate, policies and demography radically altered the sahelian land-use with a general expansion of cropland (Tappan et al., 2016). A severe drought affected the area during the 1970s and 1980s and, coupled with the overall changes in land-cover in the Sahel, led to a perceived land degradation (Miehe et al., 2010; Gangneron et al., 2024). Policy efforts made in the area to fight this phenomenon led to mixed results, due to a limited assessment of the causes of soil degradation and the overestimation of farmer economic capacities to buy agricultural inputs (seeds, fertilizers) (Syr Diagne, 1989; van Vliet et al., 2013). There is thus a necessity to assess with more details past trajectories of land-use change, and to produce realistic future scenarios

<footnote>* Corresponding author.
E-mail address: anem.dupre@outlook.com (A. Dupré).</footnote>
of Sahelian land-use for the coming decades to support effective and sustainable decision-making.

Estimating past changes in land-use is possible using high resolution remote sensing datasets (Fritz et al., 2010). This approach remains limited as data before the 1980s are very scarce, especially in the Sahel. Additionally, it does not provide insight into the causal explanation of land-use dynamics in a given context. Tappan et al. (2016) provides a high confidence dataset of land-use and land-covers for the whole Sahelian region for 1975, 2000 and 2013. The creation of this dataset required image-by-image visual interpretation of satellite data, highlighting the difficulty of using remote sensing to create land-use and cover time series with a fine resolution.

Mechanistic modeling comes as a particularly interesting approach for estimating land-use and cover changes, tackling the Sahelian problem of lack of data and formalizing the dynamics of land-use change under the effect of human and environmental factors. Developing such a model of the human-environment system allows eventually to test combined climate and policy scenarios, providing associated predictions of future land-use changes and identifying key factors of change for policy makers (Brown et al., 2004).

At the global scale, multiple models were developed to reconstruct past land-use evolutions, using statistical data such as world population or remote sensing data interpretation for space allocation (Ramankutty et al., 2002; Klein Goldewijk et al., 2017; Winkler et al., 2021). These approaches remained limited when dealing with regions like Africa, showcasing particularly a downscaling issue (Kabora et al., 2024). For example, HILDA+, a global land-use reconstruction model, concluded to the stability of areas covered by crops and forests in Senegal between 1960 and 2019 (Winkler et al., 2021), going against the documented expansion of agricultural lands and regression of forest-lands in this country (Tappan et al., 2016). Future land-use trajectories can be estimated with Integrated Assessment Models (IAMs), that are composed of modules representing technological, environmental and human systems, creating an integrated framework to model complex human-environmental networks of interactions (Guillemot, 2022). Provided with different climate and socio-economic scenarios such as RCPs (Representative Concentration Pathways) and SSPs (Shared Socioeconomic Pathways) developed for the IPCC (Intergovernmental Panel on Climate Change), they can predict future dynamics of pastoral lands and croplands at the global scale (Schmitz et al., 2014). Yet, IAM projection's quality heavily depends on the quality of the inputs and on the downscaling of climate and socio-economic scenarios (Prestele et al., 2016; Guillemot, 2022).

In the Sahel, lack of historical data makes even national scale predictions difficult to achieve due to knowledge gaps on past land-use dynamics and associated mechanisms (van Vliet et al., 2013; Yang and Cui, 2019). Recurrent failure of classical historical land-use reconstruction models to reproduce known trends in the Sahel stems from (1) the literature gap on land-use changes in the region, the only detailed studies being available at the scale of villages or landscapes and thus precluding broader-scale generalization (van Vliet et al., 2013), and (2) the difficulty to distinguish between actively cropped fields and fallows on remote sensing data when it comes to grass fallow systems in the Sahel (Tong et al., 2020).

To tackle these problems, the development of a model adapted to the Sahel is a much needed tool to reconstruct past dynamics and make predictions for the future. Different modeling approaches can be used, from statistical models to process-based ones. However, processes such as climate, plant productivity, or food demand, may interact such that it becomes necessary to account for them explicitly to produce reliable predictions on land-use. A mechanistic modeling of the system is thus preferable both to better understand the effects of identified driving forces on land-use changes (Lambin and Geist, 2006), and to use it to inform decision-making while considering different possible scenarios. Such a process-based model was proposed by Stéphenne and Lambin with the dynamic simulation model of land-use changes in the

Sudano-Sahelian countries of Africa (SALU) (Stéphenne and Lambin, 2001). This model is based on the assumption of equilibrium between land-use related resource production and demand while also taking into account cereal imports and exports. The areas covered by the different land-uses are then estimated by calculating the area needed for resources production. This model is to our knowledge the only land-use model designed to account for the specific Sahelian context. It was particularly tested using the case of Burkina Faso. Because of the scarcity of data, high uncertainty yet remained on the outputs of the SALU model, which still need to be evaluated. In addition, the SALU model was encoded on a Stella architecture without explicit detailing of the calculations (Lindfield, 1992), making it difficult to replicate results and adapt model processes. Hence, this study aims to reconstruct land-use dynamics in Sahelian conditions with a new model inspired by the processes and architecture of SALU, using Senegal as a testing ground. Senegal was chosen because of its representativity of other Sahelian countries – as illustrates the dependency of 60% of Senegalese on livestock (Eeswaran et al., 2022) – and the large amount of literature on climate and policy changes effects on land-use.

Here, we present a SALU-inspired mechanistic model destined at reconstructing historic land-use dynamics. Ranges of values for parametrizations are obtained from an extensive bibliographic search, and the sensitivity of outputs to these parameters is evaluated. The model is then applied to predict land-use evolutions between 1961 and 2020 in Senegal, and to calculate land-demand in the Groundnut basin, a senegalese well-documented region, between 1974 and 2017.

## 2. Material & Methods

The SALU model was originally encoded on a Stella architecture without full transparency of calculation steps (Lindfield, 1992). Consequently, we developed a new open-access model relying on the core assumptions proposed by Stéphenne and Lambin (2001). Previous studies using the SALU model (Stéphenne and Lambin, 2001; Rasmussen et al., 2012) were carried out under Sudano-Sahelian conditions, in Burkina Faso (although Rasmussen et al. (2012) focused on the Sahelian region of Burkina Faso only) over the period 1960–2000. Senegal and Burkina Faso have enough similarities in terms of climate, agropastoral practices and socio-economic trajectories to allow the use of the improved SALU model in Senegal (Appendix A.1). We adapted ecological and economic modules to be consistent with data stemming from the most recent literature and to better estimate vegetation productivity from grasslands and croplands (see Appendix A.2 for detailed between-model differences). The code used to run the model was archived and made fully available online (Dupré, 2024). A usage guide is provided in Appendix B.

Given a set territory with available area AREA in ha, this study focuses on three categories of land-use: veg, the fuelwood extraction area corresponding to forests and woody savannas that can be used for fuelwood extraction, past, the pastoral land, and crop, the cropland (taking into account lands left fallow for nutrient restoration). The rest of available lands, with no fuelwood resources, are considered unused and referenced as un. By assumption, these land-use cover the entirety of available land, thus satisfying Eq. (1) at each time step t.

$$ AREA = veg(t) + past(t) + crop(t) + un(t) \quad (1) $$

The theoretical framework used for our model is described in Fig. 1. Each land-use is associated to the production of one specific resource: pastoral lands produce forage, croplands produce cereals, and savannas and forests produce fuelwood. On the other hand, the same resources are consumed by livestock and the urban and rural populations. To link the consumption to the production of resources, we used the global food equation framework (Eq. (1)).

$$ population_c \times consumption\_per\_capita_{c,i} = area_{c,i} \times yield_{c,i} \\ + (imports_{c,i} - exports_{c,i}) \quad (2) $$
With c a given country and i a given product (Lambin and Meyfroidt, 2011). The model is thus founded on the assumption that production and consumption are at equilibrium when taking into account imports and exports (Fig. 1).

To estimate the surface covered by each land-use at a given year, the model starts with the needs of the population and livestock and works backwards to estimate the quantity of resources consumed and therefore, by hypothesis, produced. The surface dedicated to each land-use is then estimated so as to satisfy this production. Section 2.1 describes the calculation of the yearly resource consumption of the population. From the resource consumption, the land needed for production is estimated. We refer to this quantity as the land-demand. The territory being finite, the different land-use compete for space. Land-demands thus cannot always be satisfied. In such cases, intensification strategies come to place to ensure the production of the needed resources despite the lack of space. Section 2.2 describes the interaction between land-demands and the resulting attribution of surfaces to each land-use.

## 2.1. Calculating land-demands based on consumption

### 2.1.1. Pastoral lands

The demand for pastoral land, past_d, is calculated using an estimation of the forage consumed by livestock animals. The number of livestock animals, liv, is an exogenous time-series further described in Section 2.3. liv is given in equivalent Tropical Livestock Unit (TLU eq.), a standardized unit taking into account the feed requirement of each species relative to that of the tropical cow taken as reference. The TLU thus provides a unified framework to analyze husbandry: while camels, cattle, and horses will have similar feeding requirements (one head corresponding to one TLU), asses, goats, and sheep have lower needs with one head corresponding respectively to 0.2, 0.1 and 0.1 TLU eq. (Rothman-Ostrow et al., 2020). The quantity of forage needed for a given number of livestock can be deduced using the feeding requirements satisfied by pasture, encoded by parameter biom_conso in tonnes/year/TLU eq. (see Table 1 for parameter description). The link between production and consumption of forage is obtained through Eq. (3).

$$biom_{prod} \times past_d = liv \times biom_{conso} \quad (3)$$

On the production side, we calculate the rangeland productivity biomProd, using the relation between annual precipitation and biomass production identified in Breman and de Wit (1983) for the Sahel (Eq. (4)). Productivity is calculated as a linear function of rain, with rain an exogenous time-series described in Section 2.3.

$$biom_{prod} = a_{biom\_prod} \times rain + b_{biom\_prod} \quad (4)$$

### 2.1.2. Croplands

The demand for cropland crop_d (ha) is calculated under the assumption that croplands are dedicated to cereal cultivation. Such hypothesis is justified by the dominant place of cereals in a majority of people's diet in the region (Honfoga and van den Boom, 2003). The total demand for cropland crop_d (in ha/year) is the sum of three types of demand: crop_d = crop_subsd + crop_markd + fal_d, with crop_subsd the lands dedicated to cereal cultivation for the local population's subsistence, crop_markd the lands dedicated to cereal cultivation for the market, and fal_d the demand for land to leave unused for nutrient restoration. This postulates that, once cultivated, cereals are either directly consumed by the rural population or commercialized on the market to nourish the urban population. Part of the cereal consumption of the urban population also comes from imports. The land-demands to satisfy the needs of rural and urban populations are calculated assuming crop productions of dedicated areas balance the population's consumption:

$$cer_{prod} \times crop\_subsd = pop_{rur} \times cer_{conso} \quad (5)$$

$$cer_{prod} \times crop\_mark_d = pop_{rur} \times cer_{conso} - cer_{imp} \quad (6)$$

With cer_conso (in kg/inhab/year) the cereal consumption derived from literature (Table 1) and cerprod the cereal productivity of croplands (in kg/ha/year). cerprod, poprur and popurb, the rural and urban populations respectively, and cereal imports cerimp are exogenous time-series described in Section 2.3.

To allow for soil fertility restoration after cultivation, the land is periodically left unused, a phenomenon referred to as fallowing. Sub-Saharan systems are known to be low-input cropping systems (Gaiser et al., 2011) making rotation one of the main fertility-maintaining system. The demand for land lying fallow, fal_d (in ha/year), is calculated using the ratio between the number of years under cultivation and the number of years under fallow set by farmers for nutrient restoration, referred to as cult_freq, the cultivation frequency (see Table 1 for parameter values).

$$fal_d = cult\_freq \times (crop\_subsd + crop\_mark_d) \quad (7)$$

### 2.1.3. Fuelwood extraction areas

Finally, the demand for fuelwood extraction area veg_d is calculated using equilibrium between production of wood and wood consumption of the rural and urban populations (Eq. (8)). Land wood productivity (fuel_woodprod in m³/year/ha) is derived from multiple literature productivity estimations. Likewise, the bibliography provided intervals of values for fuel_woodconso/rur, the fuelwood consumption of the rural population, and fuel_woodconso/urb, the fuelwood consumption of the urban population, both in m³/inhab (see Table 1).

$$fuel\_wood_{prod} \times veg_d = pop_{rur} \times fuel\_wood_{conso/rur} + pop_{urb} \times fuel\_wood_{conso/urb} \quad (8)$$

## 2.2. Land attribution

The demand for the different land-uses is determined for each year. The actual land attribution is then determined depending on whether land-demand can be entirely satisfied by available areas or not. If it can, land-uses are attributed to available lands according to the demand without further calculations. Areas unattributed to any land-use are considered unused. In this case, increase of the demand leads to an expansion of agricultural lands at the detriment of first unused areas and then fuelwood extraction areas. On the other hand, if the agricultural demand is too high to be satisfied by available lands, the system switches towards an intensification mode, with conflicts arising between land-uses. The production needs are satisfied through intensification strategies and land attribution is done based on the strength of the pressure of the demand.

### 2.2.1. Expansion

During expansion, land-uses are first attributed to agricultural use: past(t) = past_d and crop(t) = crop_d. If the surface needed for expansion of agricultural land exceeds the unused area, then deforestation and land cleaning occurs, replacing savannahs and forests (veg(t)) with pastoral lands and croplands. In the reverse situation, regression of agricultural land due to lower demands leads to the emergence of unused areas that cannot be directly used as fuelwood extraction area because of the absence of vegetation cover. After 10 years, tree regrowth leads to the conversion of said unused area un(t) into fuelwood extraction area veg(t) (Gaudreau and Gibson, 2015; Stéphenne and Lambin, 2001).

Initially, all lands unattributed to an agricultural use is categorized as fuelwood extraction area, meaning it is assumed that the land-cover produces wood resources. Thus, unused area un(t) is initially set to 0 and only emerges from regression of agricultural demand. We assume that the part of the fuelwood extraction areas corresponding to the demand veg_d cannot be deforested, which sets a limit to agricultural expansion
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Theoretical framework. On the one hand, lands produce resources (forage, cereals, fuelwood) and on the other the population and livestock consume resources. The production/consumption equilibrium hypothesis links the consumption of resources to the land used for production. Provided the exogenous time-series presented in Fig. 2, this framework allows for an estimation of the surface covered by each land-use.</figcaption></figure>

and allows for satisfaction of a part of energetic needs. The decrease in surface of forests and savannas used for fuelwood extraction is limited for different reasons: (1) the local population protects this resource for domestic consumption of fuelwood, national parks, reserves or sacred forests (Benjaminsen, 1993) (e.g. some villages banned goats in favor of chickens to protect tree seedlings (Gaudreau and Gibson, 2015)), (2) some regions cannot be accessed due to inaccessibility or high incidence of tsetse flies (Seck et al., 2010) (Okello et al., 2022). Since the protection of fuelwood extraction areas only consider the demand at the year of calculation, it does not guarantee the satisfaction of the demand in the next years if the demand increases. In this case, the population is hypothesized to compensate using other sources of energy.

### 2.2.2. Intensification

If deforestation is not enough to support agricultural expansion, meaning (*AREA* − *past*<sub>*d*</sub> − *crop*<sub>*d*</sub>) < *veg*<sub>*d*</sub>, then the system switches towards intensification. To satisfy the food demand, different intensification strategies are put in place in parallel by the population: yield increases through the addition of fertilizers and a higher number of working hours, a decrease in pastoral lands and reduced fallows. The model takes into account all three strategies simultaneously.

First, the increase in yield is taken into account through the exogenous time-series for cereal productivity *cer*<sub>prod</sub>. Second, the demand for pastoral lands is decreased through a switch in the livestock consumption patterns: during expansion, only 1/3 of livestock's biomass
<caption>**Table 1**</caption>

Available data on model parameters. Presentation of model parameters along with available data derived from literature. The SALU values column presents the values used in Stéphenne and Lambin (2001). (see Behnke and Scoones (1993), Bonetti and Jouve (1999), de Leeuw and Tothill (1990), Drigo (2004), Haggblade et al. (2017), Le Houérou and Hoste (1977), Nutini et al. (2011), Picard et al. (2006))

<table><thead><tr><th>Parameter</th><th>Description</th><th>Unit</th><th>SALU values</th><th>Literature range</th><th>Sources</th><th>Conditions</th></tr></thead><tbody><tr><td><i>biom_conso/expansive</i></td><td>Livestock forage consumption under expansive agriculture</td><td>tonnes/ eq. TLU/year</td><td>4.6</td><td>[3.4, 6.1]</td><td>1: Le Houérou and Hoste (1977)<br>2: Behnke and Scoones (1993)<br>3: de Leeuw and Tothill (1990)</td><td>1: African Sahelo-Sudanian zone, 1977<br>2: African savannahs, 1983<br>3: Sub-Saharan Africa, 1993</td></tr><tr><td><i>biom_conso/intensive</i></td><td>Livestock forage consumption under intensive agriculture</td><td>tonnes/ eq. TLU/year</td><td>2.3</td><td>[1.7, 3.0]</td><td>1: Le Houérou and Hoste (1977)<br>2: Behnke and Scoones, (1993)<br>3: de Leeuw and Tothill (1990)</td><td>1: African Sahelo-Sudanian zone, 1977<br>2: African savannahs, 1983<br>3: Sub-Saharan Africa, 1993</td></tr><tr><td><i>cer_conso</i></td><td>Cereal consumption of the Senegalese population</td><td>kg/inha b/year</td><td>360</td><td>[157, 250]</td><td>Haggblade et al, 2017</td><td>Sahelian West Africa</td></tr><tr><td><i>cult freq</i></td><td>Cultivation frequency driving the crop-fallow cycle</td><td>Year<sup>-1</sup></td><td>2</td><td>[1, 3]</td><td>Bonetti and Jouve (1999)</td><td>Sub-Saharan Africa, 1999</td></tr><tr><td><i>fuel_wood_conso/rur</i></td><td>Fuel wood needs: rural consumption</td><td>m3/inh ab/year</td><td>0.65</td><td>[0.27, 0.36]</td><td>Drigo (2004)</td><td>Senegal, 1992 and 1996</td></tr><tr><td><i>fuel_wood_conso/urb</i></td><td>Fuel wood needs: urban consumption</td><td>m3/inh ab/year</td><td>0.85</td><td>[0.05, 0.10]</td><td>Drigo (2004)</td><td>Senegal, 1992 and 1996</td></tr><tr><td><i>fuel_woodprod</i></td><td>Fuelwood productivity of natural vegetation</td><td>m3/yea r/ha</td><td>0.75</td><td>[0.3, 1.2]</td><td>1: Picard et al. (2006)<br>2: Drigo (2004)</td><td>1: Dry savannahs in Africa<br>2: Senegal, 1982 - 1984</td></tr><tr><td><i>a_biomprod</i></td><td>Slope of <i>biomprod</i></td><td>Tonnes /ha/m</td><td>0.00375</td><td>[0.002, 0.006]</td><td>Nutini et al. (2011)</td><td>Senegal and Niger, 2005 - 2009</td></tr><tr><td><i>b_biomprod</i></td><td>Intercept of <i>biomprod</i></td><td>Tonnes /ha</td><td>0.15</td><td>[0.04, 0.77]</td><td>Nutini et al. (2011)</td><td>Senegal and Niger, 2005 - 2009</td></tr><tr><td><i>crop_past_ratio</i></td><td>Crop/pasture trade-off coefficient</td><td>None</td><td>0.4</td><td>[0, 1]</td><td></td><td></td></tr></tbody></table>

consumption comes from crop residues. Under intensification, the consumption of crop residue can rise up to 2/3, reducing the need for land and leaving space for cultivated fields. This switch in consumption patterns is encoded in the model with the parameter *biom_conso* taking the value *biom_conso/expansive* when agriculture is expansive, and *biom_conso/intensive* during intensification (Table 1). Such strategy is not sufficient to withstand high demands for forage and would thus lead to livestock losses if fodder is not imported. These losses would not necessarily appear on Fig. 2 time-series, since the stocks could be renewed from one year to the next. Since imported fodder was not considered in the model, livestock losses were not estimated. Finally, the trade-off between reduction of pastoral lands and reduction of fallows is encoded with Eq. (9) in which we introduce *crop_past_ratio*, a dimensionless coefficient of priority for croplands over pastoral lands.

$$fal(t) = fal_d - (crop_d - crop(t - 1) + past_d - past(t - 1) - un(t)) \times (1 - crop_past_ratio) \quad (9)$$

With *fal*<sub>d</sub> calculated with Eq. (7). A higher pressure for land translates in higher *crop*<sub>d</sub> and *past*<sub>d</sub> values and consequently, in a decrease in lands left fallow *fal*(*t*). The lands attributed to active cultivation are hypothesized not to be affected by the conflicts between land-uses, meaning *crop_subsd* = *crop_subsd*<sub>d</sub> and *crop_mark* = *crop_mark*<sub>d</sub>. The area covered by fallows thus decreases to leave space for pastures. The decrease in lands left fallow continues with increasing crop and pastoral demands, until no lands are left unused for nutrient restoration (*fal*(*t*) = 0).

## 2.3. Exogenous variables

Senegal was chosen as a testing ground for the application of the updated model, as it is a country with good representativeness of agroclimatology in West Africa. The country covers three bioclimatic zones with a pronounced rainfall gradient: Sahelian (arid) with rainfall from

100 mm/year to 500 mm/year, Sudanian (semiarid) with rainfall from 500 to 1000 mm/year, both with a single rainy season of 3 to 4 months, and Guinean (subhumid/humid) with both uni- and bimodal patterns depending on location (1000 to 1500 mm/year). This heterogeneity in local climates, combined with social and economic differences within the population, causes land-use and land-cover to change drastically between regions (Tappan et al., 2004).

The model requires parameter values for the ten parameters of Table 1 and time-series for each of the six exogenous variables displayed in black in Fig. 1. Compared to other regions of the world, available data for parameterization for the Sahel remain heterogeneous and sparse. Despite this limitation, analysis of the literature allowed us to gather the information presented in Table 1, and in particular ranges of values for each parameter. Two scales were considered for analysis: national, on the entirety of the Senegal territory, and regional, on the Groundnut basin, defined here as the combination of administrative regions Diourbel, Fatick, Kaffrine, Kaolack and Thies. Although this zone heavily relies on agriculture, with 60% of Senegalese farms concentrated in the area, which represents about a fifth of the national surface, little is known on the quantitative evolution of its land-uses (Bourgoin et al., 2023; Gangneron et al., 2024). Downscaling the model could thus provide valuable information and new perspectives on the use of modeling approach for sahelian land-use scenarios. We used the geographic divisions described by the global administrative areas (GADM) database (Hijmans, 2022). Data availability conditioned the analysis to cover the period 1961 to 2020 at national scale, and 1974 to 2017 at regional scale. Exogenous variables are presented in Fig. 2 (data sheets are provided in tables S1 and S2).

### 2.3.1. National scale

The time-series used as exogenous variables at national scale mostly come from the Food and Agriculture Organization of the United Nations (FAOSTAT) database (FAO, 2024). The dataset provides us with the rural and urban population (*pop*<sub>rur</sub> and *pop*<sub>urb</sub>), as well as the mean
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Exogenous variables for Senegal and the Groundnut basin. Blue corresponds to national scale, red to the Groundnut basin. Data is presented per unit space for between-scale comparability. The national rangeland productivity values are calculated over 1000 randomly drawn parameter sets using Eq. (4). For cereal imports, the same time-series is used for national and regional scale. Sources are the following: at national scale, the FAO for population densities, the livestock density, the crop yield, and cereals imports (FAO, 2024); ERA5 for rain data (Hersbach et al., 2020). At regional scale, the ANSD for population densities (ANSD, 2006, 2016, 2023); the GLW database for the livestock density (Gilbert et al., 2018); the STICS model for the crop yield (Brisson et al., 1998); the STEP model for the rangeland productivity (Mougin et al., 1995); the ANACIM for rain data (ANACIM, 2017).</figcaption></figure>

national cereal yield (*cer*<sub>prod</sub>), the livestock (*liv*), and cereal imports (*cer*<sub>imp</sub>). The cereal yield accounts for fonio, maize, millet, rice and sorghum. Livestock is obtained using the number of animals for species asses, camels, cattle, goats, horses, and sheep. Finally, the net cereal imports are estimated by subtracting imports from exports using categories Cereals and Cereal preparations defined by the Central Product Classification (CPC) (United Nations, Statistics Division, 2015).

We chose to use the rain time series provided by ERA5 (Hersbach et al., 2020). Although ERA5 data can present seasonal deviations from observations, in particular in July due to the difficulty to model mesoscale convective systems in western Africa (Lavers et al., 2022), it is a reference weather model for the past decades. Data is available at a 31 km scale. We used the mean ERA5 values across Senegal territory (fig. S1). The rangeland productivity (*biom*<sub>prod</sub>) could then be estimated using Eq. (4).

### 2.3.2. Regional scale

At the scale of the Groundnut Basin, the ANSD, the Senegalese statistics and demography agency, provides rural and urban survey data for years 1976, 1988, 2002, and 2013 to 2017 (ANSD, 2006, 2016, 2023). Missing data is estimated using splines. We hypothesize that the cereal imports per urban inhabitants was the same at national and regional scale. That is to say that $cer_{imp/reg} = cer_{imp/nat} \times pop_{urb/reg}/pop_{urb/nat}$.

Regional livestock values are extracted from the Gridded Livestock of the World (GLW) database for years 2009 and 2015 (Gilbert et al., 2018). The GLW database comes as a response to the scarcity of subnational livestock data through the use of a per-pixel attribution model.

Statistical inferences are made based on environmental predictor variables to estimate how livestock distributes within census units, such as countries. Species considered were cattle, goats, horses, and sheep, with a spatial resolution of approximately 10 km. The average proportion of national livestock situated in the Groundnut basin is calculated using GLW data and national livestock values for the corresponding years. This proportion is hypothesized to be constant. As a result, for each year, approximately 38% of national livestock is attributed to the Groundnut basin.

For the regional study, specific Sahelian vegetation models were used to simulate grass and millet growth, using meteorological conditions and soil characteristics as inputs. The cereal yield (*cer*<sub>prod</sub>) is estimated using the multidisciplinary simulator for standard crops model (STICS) (Brisson et al., 1998). Calculations are made for millet using the STICS calibration provided by Sow et al. (2024). The seasonal herbaceous layer is computed with the STEP model (Mougin et al., 1995) to estimate the rangeland productivity (*biom*<sub>prod</sub>), instead of Eq. (4). Using this model is not possible at national scale because of the presence of multiple bioclimatic zones, STEP being only adapted to Sahelian conditions. STEP was proven to simulate the Sahelian herbaceous growth with high reliability at both local and regional scale (Pierre et al., 2015, 2016). The possibility to use the model here thus allows us to obtain more dependable results at regional scale. Rain time-series (fig. S1) are obtained by averaging ANACIM weather station data over Dakar, Diourbel, Kaolack, and Kongheul (ANACIM, 2017). We only have access to months May to November, with low
impact on yearly values since rainfall is rare December to April, the wet season only beginning in May (Fall et al., 2006). Temperature time-series were provided by ERA5, from the grid cell centered on the city of Bambey. The soil composition is estimated through field work on a representative site of the region's soil (Bambey), with approximately 5% of clay. Simulated biomass productivities highlight that Eq. (4) tends to overestimate the rangeland biomass productivity (Fig. 2). This overestimation cannot be explained by lower rainfall values in the Groundnut basin, since regionally-averaged rainfall is greater than nationally-averaged rainfall (fig. S1).

## 2.4. Analysis outline

The 10 parameters have high value uncertainty (Table 1). Each one thus contributes to the output variance. With the possibility to rank parameters according to their relative significance, a sensitivity analysis is carried out to investigate the impact of each model parameter uncertainty on the model outputs. The goal is to identify parameters with low effect, and establish which parameters to investigate to decrease model output variance in future studies using this model.

We decided to use the δ-sensitivity measure developed by Boronovo (2007). This density-based sensitivity analysis method quantifies the normalized expected shift in the distribution of an output induced by one parameter over all its possible values. δ-indices range from 0 to 1, with 0 corresponding to an output independent of the parameter at hand and 1 to a case where the parameter entirely determines the output. Higher values thus correspond to a higher effect. This method was chosen due to its minimum computational cost and its consideration of the entire distribution of inputs and outputs. A Sobol total-order sensitivity analysis was conducted to assess whether interactions among parameters affected the results (fig.S2). 10000 samples of parameter sets are generated, and associated model outputs are calculated using national exogenous time-series. Then for each output land-use, δ-indices are computed using the SCALib module (Cassiers and Bronchain, 2023). Results are discussed in Section 3.1.

Subsequently, 1000 parameter sets are sampled for application of the model to land-use prediction. First at the national scale, with exogenous time-series of Section 2.3.1, then at the scale of the Groundnut basin with Section 2.3.2 data. The Python language is used for the entirety of our study. Modules NumPy and Pandas are used for computation, and data manipulation (Harris et al., 2020; McKinney, 2010). Both the sensitivity analysis and the model application require exploring of the parameter space through rigorous sampling. The SciPy python module (Virtanen et al., 2020) is used for this purpose, with a uniform probability distribution on parameter intervals. The code is made fully accessible (Dupré, 2024).

# 3. Results

## 3.1. Parameter sensitivity

δ-indices are presented in Fig. 3. *crop_past_ratio*, *biom<sub>conso/expansive</sub>* and *biom<sub>conso/intensive</sub>* are parameters that only intervene during either expansion and intensification, contrary to other parameters that are used at each step regardless of the intensification status. Their global effect on the model output is thus expected to be impacted by the number of years spent in agricultural expansion or intensification. On the other hand, the difference in effect between *fuel_wood<sub>conso/rur</sub>* and *fuel_wood<sub>conso/urb</sub>* could be explained by the consistently higher proportion of the population living in rural areas (Fig. 2), making *fuel_wood<sub>conso/rur</sub>* a more influential parameter.

With a very low mean delta index of 0.034, *crop_past_ratio*, the crop/past priority ratio, is the least influential parameter (Fig. 3) and has practically no effect on model outputs when taking into account interactions (fig.S2). No information was known on this parameter, and refining this parameter's interval of values would require extensive

analysis of the conflicts between land-uses. Consequently, the low effect of this parameter's uncertainty on the model output will facilitate the work of reducing model output uncertainty in future studies.

The cereal consumption, *cer<sub>conso</sub>* stands out with a very high effect on model outputs and in particular on *crop_mark* and *crop_sub*, *Delta* indices being lower than 0.04 for the other parameters when considering *crop_mark* and *crop_sub*, *cer<sub>conso</sub>* is even the only influent parameter for these outputs. The uncertainty on *cer<sub>conso</sub>* thus highly affects the land-use reconstruction, indicating that investigating this parameter should be prioritized when looking to reduce model output uncertainty. At a lower level, *cult_freq*, *a<sub>biomProd</sub>* and *biom<sub>conso/expansive</sub>* also have significant effects, distributed over multiple outputs. These parameters maintained a high impact even when parameter interactions were included in the analysis (fig.S2). Investigating these parameters could thus also help reduce model output uncertainty.

## 3.2. National scale

### 3.2.1. Land-use evolutions

Starting at the national scale for which the model was designed, results are presented in Fig. 4 with panel b presenting the median land-use values across all simulations. The stacked medians (Fig. 4b) do not amount to the available area represented as a dotted line because median values for each land-use are not necessarily from the same simulation, where Eq. (1) applies. Model outputs present a very high variability, as illustrate fuelwood extraction areas (*veg*) with values ranging from 0% to 60% of covered territory from 1972 to 1991 (Fig. 4a). However, land-use evolution trends can be identified through the general dynamics across simulations.

Initially, fuelwood extraction areas (*veg*) cover more than half of national territory, whereas in 2020, that proportion gravitates around 20% of occupied territory (Fig. 4a). Until the 1990s, the expansion of agricultural lands leads to deforestation, with lands never left unused enough time to allow for the vegetation to reform. Fuelwood extraction areas reach their minimum value at the end of the 1990s. The associated decrease in unused areas indicates that the territory reaches a state of land-use saturation with a necessity to rely on intensification strategy to ensure the satisfaction of consumption needs.

Due to the strong Senegalese demographic increase (Fig. 2) and the model hypotheses, the amount of land dedicated to *crop_mark* and *crop_sub* increased to satisfy the higher consumption needs of both the rural and urban population. However, this increase stops in the 2000s to leave in place a plateau and even a decrease after the 2010s. This phenomenon is the result of the high increase in cereal yield observed on FAO data, with values doubling between the 2000s and the 2020s (Fig. 2). The necessary quantity of lands for equal production thus significantly decreased.

In the case of fallows, allocated lands slightly increase until 1998. Consecutively, the 2000s show a decline in fallows, with medians diminishing of one third. In addition, a considerable number of simulations predict a collapse of fallow lands, with the lower quartile approximating zero from 2003 to 2020. Since *crop_mark* and *crop_sub* strongly increased from the 1960s to the 2000s, the low increase of fallowing land *fal* indicated that the crop-fallow cycle was already impacted by pressure for land before the decrease in fallow lands. This phenomenon will be further explored in Section 3.2.2.

Finally, pastoral lands switch from covering less than 20% of national territory in 1961 to covering approximately half available lands in 2020. Pastoral lands increased until the 1970s, plateaued until the mid-1980s, before continuously increasing until the end of the simulation time. This evolution is coherent with the evolution of livestock (Fig. 2). Livestock increased, until the severe 1970s and 1980s droughts caused high mortality and thus a plateau and slight decrease of animal populations. Then, starting in the mid-1980s, livestock continuously increased. In addition, the decrease in cultivated lands as a result of
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Differences in model parameter sensitivity. Delta indices are calculated over model outputs for 10000 randomly sampled parameter sets drawn from intervals in Table 1. National exogenous variables are used for the simulations. All indices have confidence intervals below 0.003.</figcaption></figure>

the increased cereal yield left enough space for pastoral land to expand even in the states of territory saturation occurring since the 1990s.

To evaluate the credibility of the predicted land-use evolutions, we compared model outputs to independent data from (Tappan et al., 2004) and (Tappan et al., 2016). These studies are based on an image-by-image photo interpretation of satellite data. We displayed data points for 1965, 1975, 2000 and 2013 on Fig. 4b and provided the trends found in (Tappan et al., 2004) in figure S3. They worked with a mix of land-use and land-cover classes, with no exact correspondence with the type of output we study. Their cultivated areas and fallows class (4b) corresponds, in the model, to the sum of *crop_subsets*, *crop_mark* and *fal*. Forests, steppes, and savannas, on the other hand, can correspond to either *un*, *past*, or *veg* depending on the attributed land-use and should thus be considered as an upper bound for the sum of these three outputs. Comparison between the four data points and the median outputs showed the correct orders of magnitude of model predictions, with a tendency of cropland values to be overestimated by the model. The trends found in figure S3 were also coherent with our model outputs with a slight decrease in the forest, steppes and savannas class and a slight increase in the cultivated lands class. In addition, three studies were considered as reference points : FAO (1995) that uses country level inventory reports to estimate the forest cover in 1990 (Fig. 4a, veg), Mazur et al. (2023) where tree cover is estimated for year 2000 by combining multiple existing maps (Fig. 4a, veg), and Gumma et al. (2024) where Sentinel-2 satellite imagery, ground data, and spectral matching techniques are used to estimate land-use and land-cover classes in 2020 (Fig. 4a, past and veg). Whilst all three studies are coherent with the results found for the fuelwood extraction area class, our estimation of the surface of pastoral lands greatly exceeds that found in (Gumma et al., 2024).

### 3.2.2. Land-use effects

To further analyze the implications of predicted outputs, we studied intensification proxies used as indicators of land-use impacts. Looking at the yearly proportion of simulations in intensification mode (Fig. 5a) confirmed the gradual switch to intensification observed in Fig. 4a: the agricultural system is under continuous expansion until the 1970s. Consecutively, peaks of territory saturation start occurring, with agricultural expansion still holding a predominant place. From then the

probability of the intensification state gradually increases and from the mid-1990s onward, the intensification state predominates.

Looking at the cultivation frequency, that is to say the number of years cultivated lands are left fallow, simulations predict values between one and three years until the mid-1990s (Fig. 5b). Though intensification starts occurring before the mid-1990s, the cultivation frequency is only impacted since 1995 with the lower whisker decreasing until reaching 0. Some simulations thus predict a reduction of the cultivation frequency with a possible negative impact on lands. However, the first quartile never reaches values beneath 1 indicating that in at least 75% of cases, the lands are left fallow at least one year despite the occurrence of territory saturation.

Livestock on the other hand seem to have been greatly impacted by territory saturation (Fig. 5c). Starting from the mid-1990s, pastoral lands stop satisfying the two thirds of livestock consumption indicating that crop residues must have been used as a substitute for the missing forage. A significant proportion of simulations even predict a satisfied consumption below 1/3 of consumption needs, meaning without an economic capacity to compensate using fodder, livestock losses are likely to have occurred.

### 3.3. The Groundnut basin's land-use demands

Having applied the model at national scale, we downscaled the analysis to the Groundnut basin. The 1000 previously sampled parameter sets were used, in addition to the regional exogenous time-series (Fig. 2). The corresponding land-use demands are presented in Fig. 6. In 1974, the demand for agricultural lands, excluding fallows, already amounts in median to the entirety of the region's surface (Fig. 6b). In parallel, the hypothesis of fuelwood extraction area preservation would require 10 to 50% of that surface to be attributed to *veg* (Fig. 6a). All land-demands increase with time during the period of analysis (Fig. 6a). Notably, the 1991 and 2014 drops in yield (Fig. 2) cause peaks in *crop_subsets* and *crop_mark* demands (Fig. 6a). Consequently, while the global increase in demand causes the cumulated median demand to reach twice the available territory, years 1991 and 2014 show peaks at approximately thrice the available land (Fig. 6b). This land-demand calculation shows the impossibility to satisfy consumption demands using only the Groundnut basin's available territory, even
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** National land-use evolution. (a) **Model outputs.** Box plots of simulation outputs for 1000 randomly sampled parameter sets drawn from intervals in Table 1. Output classes are *fal* (fallows), *crop_subsl* (croplands satisfying subsistence needs), *crop_mark* (croplands cultivated for the market), *un* (unused areas), *past* (pastoral lands), and *veg* (fuelwood extraction areas). Results are displayed as the fraction of national territory occupied by each land-use. Boxes represent the first and third quartiles of data. Whiskers indicate the first quartile minus 1.5 times the interquartile range, and the third quartile plus 1.5 times the interquartile range. Medians are indicated in red. Data points from FAO (1995), Gumma et al. (2024) and Mazur et al. (2023) are added for comparison. (b) **Trend overview.** Evolution of land-use occupation at national scale (medians). For comparison, data from Tappan et al. (2004, 2016) and Gumma et al. (2024) are added as bar plots for years respectively (1) 1965 (2) 1975, 2000, and 2013, and (3) 2020. The dotted line indicates the available national surface.</figcaption></figure>

when considering known intensification strategies. Such a high demand
 renders obsolete our land-attribution framework, since resources from
 outside the Groundnut basin need to be imported to satisfy the demand.
 Tappan et al. (2016) data show that the Groundnut basin is a primarily
 agricultural region with approximately 60% of the territory covered by
cultivated lands and fallows (Fig. 6b), whereas croplands only cover
 approximately 17% of the national area (Fig. 4b). Forests, steppes,
 and savannas declined from 37% in 1974 to 28% in 2013 (Fig. 6b),
 coverages far from the 80% of territory covered by this class nation-
 ally (Fig. 4b). Comparing national and regional exogenous time-series
 provides insights into the exploding land-demands of the Groundnut
 basin. To begin with, the demographic pressure is very high regionally.
The rural density in particular is consistently approximately three times
higher in the Groundnut basin relative to nationally, explaining the
high *crop_subsl* values in Fig. 6b. In addition, 38% of national livestock
is hypothesized to be situated in the Groundnut basin, whereas the
Groundnut basin only represents 18% of the national surface. As a re-
sult, the livestock density is approximately two times higher regionally
(Fig. 2).

Tappan et al. (2016) data and the exogenous time-series paint
the picture of a highly populated region, where lands are primarily
attributed to agricultural use. However, considering the magnitude of
land-demands, the precise yearly evolution of land-uses could not be
estimated using this model.

4. Discussion

The present study aimed at developing and applying a land-use re-
construction model to Senegal. The availability of time-series usable as
model inputs conditioned the analysis to the period between 1961 and
2020 at national level, and between 1974 and 2017 in the Groundnut
basin (Fig. 2). In the Groundnut basin, however, the model could not
be applied due to demands far exceeding available space, requiring
additional hypotheses to model regional dynamics (Fig. 6).

4.1. *National scale*

Though the literature available for validation is limited, at national
scale both (Tappan et al., 2004, 2016) corroborate our results (Fig.
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** National land-use effects. The 1000 simulations ran at national scale are analyzed through three intensification proxies: the (a) **saturation frequency**, meaning the fraction of simulations with no space left for agricultural expansion at each year, the (b) **cultivation frequency**, corresponding to the ratio between *fal* and *crop<sub>mark</sub>* + *crop<sub>subs</sub>*<sub>d</sub>, and the (c) **percentage of livestock feeding requirements satisfied by pastures**. The latter is obtained by dividing the produced forage *past<sub>d</sub>(t)* × *biom<sub>prod</sub>(t)* by the total consumption needs 3/2 × *biom<sub>conso/expansive</sub>* × *liv(t)*. Panels b and c display results as box plots with darker points indicating medians.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Land-demand evolution in the Groundnut basin. (a) **Model predictions** for *past*<sub>d</sub> (the demand for pastoral lands), *crop_subsd* (the demand for croplands for the rural subsistence needs), *crop_subsd* (the demand for croplands for the market), and *vegd* (the demand for fuelwood extraction areas). *past*<sub>d</sub> is calculated under the hypothesis of minimal livestock consumption (with 2/3 of consumption needs satisfied by crop residues). Box plots of simulation outputs are displayed for 1000 randomly sampled parameter sets drawn from intervals in Table 1. Results are displayed as the ratio between land-demand and available territory. Medians are plotted in red. (b) **Trend overview.** Median evolution of land-demands. For comparison, Tappan et al. (2016) data are added as bar plots for years 1975, 2000, and 2013 (Tappan et al., 2016). The dotted line corresponds to the Groundnut basin surface.</figcaption></figure>
4b & fig. S3). The decrease in fuelwood extraction areas predicted by our model is coherent with values found in FAO (1995), Mazur et al. (2023), and Gumma et al. (2024), though a limit of this comparison is that these articles provide tree cover values and not land-use. Results are also coherent with the 40% decrease in vegetated lands between 1960 and 2015 observed in Gaudreau and Gibson (2015). For pastoral lands, Gumma et al. (2024) found values far lower than ours with a coverage of 1% of the territory for 2020 against a median of 45% in our model. Since the attribution of classes in this article, apart for crops, are not detailed, it is difficult to know whether our results can be directly compared. In their study, no pastures seem to have been detected in the groundnut basin though such practices are known to be present on this territory (Badji et al., 2013). As a result, a possibility for the divergence between our results could be that Gumma et al. (2024) used a more restrictive definition of pastures, for example not taking into account transhumance. However, given the magnitude of divergence between our results, our model could be overestimating the surface covered by this land-use. Further research, and in particular field studies, thus needs to be led to validate our model.

It is not known since when intensification strategies have been implemented in Senegal, nor the extent of such intensification. The only studies available are at local level, or focus on intensification as an agricultural innovation and not the result of land saturation (Sall, 1992; Santiago et al., 2023). When applying SALU to Senegal, Lambin (2001) predicted a switch to continuous intensification starting from 1976, but no uncertainty was taken into account (Lambin, 2001). Through the consideration of parameter ranges, our analysis shows that although territory saturation may have occurred since the 1970s, the shift from agricultural expansion to agricultural intensification is likely to have occurred in the mid-1990s. Field studies report concordant findings with farmers found to respond to the increasing demographic pressure with the cultivation of all available arable lands and a decline in fallowing times (Masse et al., 2018; Gangneron et al., 2024). With such decrease in fallows and insufficient fertilizer to compensate, arable lands' fertility was found to decrease (Niane-Badiane and Szempruch, 1998; Gangneron et al., 2024). Although a decrease in fallowing time is predicted by our model from the mid-1990s (Fig. 5b), 75% of simulations did not predict a decrease in fallowing times below 1 year. This indicates a possible overestimation of the surface covered by fallows in our model.

## 4.2. Regional scale

Downscaling the model to the Groundnut basin using the same framework highlighted limitations of the model because of the very high consumption demands considering the size of the territory. The hypothesis according to which the demand of the population is satisfied by the local production inside the considered area does not hold in this case, which exhibits extreme characteristics in terms of concentration of human and livestock population, compared to the national averages. It thus clearly stands out of the usual range of the SALU model. Indeed, the demographic pressure on lands and resources in the Groundnut Basin have concerned the public authorities as soon as the 1930s. Despite measures to relocate populations to available arable lands, both colonial authorities and, after the independence, the Senegal government, failed at decongesting the region and hindering the demographic increase. The high agricultural land-demands found in this study corroborate the pressure for resources in this region. Now, although studies predicted as soon as the 1980s a deterioration in the agricultural system and the appearance of "serious symptoms of overcrowding" (Lericollais, 1983), agriculture remains economically viable in the region, and no exodus movement were recorded (Lalou and Delaunay, 2015). Yet, income diversification is mandatory for most households in the groundnut basin as non-agricultural activities offer an alternative to the unsatisfying food production. Migration of one or several members of each households is a common practice, it

can be seasonal and regional (migration to the cities during the dry season) or long term migrations abroad (to Europe or the USA)(Alobo and Bignebat, 2017; Voss, 2022; Garambois et al., 2024). The rural population is not self-reliant for the production of the cereals needed for their consumption. Through mutual aid networks, workers provide the share of cereals lacking and act as buffer in the event of production downturns. The parallel decline in appeal of the traditional agricultural work for younger family members contributed in a turn to non-farming economic activities, locally or through migration, for an economic and social emancipation (Lalou and Delaunay, 2015; Gangneron et al., 2024). Another diversification of the agricultural systems appeared as livestock breeding and fattening. It was developed during the 1980s in the groundnut basin as a way to build savings, generate income and act as a safety net in a period with uncertain rainfalls and yields. This practice remains one of the main adaptations of the area to growing pressure on the land (Piraux and Buldgen, 1997; Alobo and Bignebat, 2017; Masse et al., 2018; Voss, 2022; Garambois et al., 2024). With this diversification in economic activities, families became less dependent on agriculture for their needs. This change in economic activities led to a lower demand for lands to satisfy consumption needs.

In addition, agriculture in this region is organized around two crops: millet, mainly cultivated to satisfy subsistence needs and thus accounted in the model, and groundnuts, a cash crop historically established through colonial influences (Gangneron et al., 2024). Most Senegalese groundnut growing areas are located in the Groundnut basin and the crops are mainly destined for exports and thus provide an income for the rural population to satisfy their consumption needs (Georges et al., 2016). In fact, crop diversification and larger inclusion of cash crops is the main agricultural adaptation adopted by farmers of the groundnut basin when faced with climate or social difficulties (Diallo et al., 2022; Voss, 2022). Though the production of groundnut tends to decrease, agriculture has seen a rise in its monetization. Watermelon cultures, and market gardening, for example, are increasingly implemented, and the money generated is used to buy rice, school supplies or other essential items (Gangneron et al., 2024). Without a specific economic module in the model, this phenomenon is not taken into account. The issues raised when applying the SALU model to the Groundnut Basin thus partly result from an overestimation of the part played by subsistence crops in the rural inhabitants' consumption. In addition, we used specific sahelian vegetation models to estimate crop and grass growth, yielding a lower vegetation production than with Eq. (4) used at the national scale.

## 4.3. Limitations

Simplifying hypotheses were used in our model. For example, wood productivity, accounted through the constant *fuel_wood<sub>prod</sub>* is known to depend on rainfall, though there is no simple equation linking one to the other (Brandt et al., 2019). Similarly, the consumption of cereal, *cer<sub>conso</sub>* was considered a constant without taking into account possible variations since the 1960s. However, the prevalence of undernourishment varied considerably during our period of analysis, with levels decreasing from 31.5% between 1997 and 1999 to 10% between 2014 and 2016 (Toure, 2019). As a consequence, the demand for cereals, and thus for croplands, is expected to be lower than predicted in our model in the beginning and to rise across the years with the decrease in undernourishment prevalence. Taking into account undernourishment could thus lead the switch to intensification to occur later. In addition, we neglected spatial effects and in particular the fragmentation of landscapes. As a consequence, crop residues are always considered available for livestock fodder even though most pastoralism practices are localized at the center north of Senegal, and thus sometimes very far away from croplands (Bakary et al., 2019). Finally, the entirety of Senegal's lands are considered available for farmers to use. However, a large part of the territory does not benefit directly to the population because of large-scale land acquisitions in the agricultural
sector, Sub-Saharan Africa being the world's most targeted area for land investments (Interdonato et al., 2020; Bourgoin et al., 2023). In 2016, just accounting for foreign investors, 3% of arable lands were declared under contract (Harding et al., 2016). Consequently, our results are to be interpreted with consideration for simplifying hypothesizes and the exclusion of certain factors of influence.

Finally, a main limitation of our model is the scarcity of high-quality historical data in the Sahel. Model outputs could thus be impacted by the data used for parameterization (Table 1) and of input time-series (Fig. 2). In some cases, the data is missing. For example, due to lack of quality time-series for regional livestock, the proportion of national livestock situated in the Groundnut basin was estimated based on statistical inferences for years 2009 and 2015. This method lacks dynamism and could thus lead to error accumulation. In other cases, the quality of data cannot be assessed as it is the case for data collected through official surveys. Although this method is a powerful tool to collect data and to make population level inferences (Couper, 2017), in Senegal, less than 10% of households declare their agricultural activity, making this field predominantly informal (DAPSA, 2021). With no high accuracy historical time-series accessible, agricultural data thus mainly comes from estimations.

### 4.4. Perspectives

To alleviate the problem of scarcity of high quality historical data, further research could be led on parameter value and input time-series. With the sensitivity analysis of Section 3.1, parameters which uncertainty increase the more the model output variance (e.g. cereal consumption per inhabitant and cultivation frequency) have been identified. Further research on these parameters would thus contribute to diminishing model output uncertainty and should be considered as priority axes of research to improve sahelian land-use modeling. Field experts and historians could also be consulted to generate alternative plausible dynamics for the input time-series of Fig. 2. Launching simulations with these different input time-series would allow for a further consideration of uncertainties over the model outputs.

Since the end of the Senegalese agricultural program in the 1980s, the decrease in fallow associated with insufficient manure and limited possibility to afford fertilizers jeopardized the sustainability of land exploitation, increasing for example the risk of land degradation (Miehe et al., 2010). All the more so that lands in this region are exposed to wind erosion resulting in lands presenting no plant life, root structures nor organic matter (Niane-Badiane and Szempruch, 1998). Degraded lands cannot be used for agriculture without time or costly interventions to rebuild soil fertility, they thus represent a land category different from the unused areas of our model. Since our model predicts the pressure on cultivated land through the crop fallow cycle, it would be interesting to integrate the economic capacity of farmers to compensate nutrient loss through manure and fertilizers (Gangneron et al., 2024). This module would predict the magnitude of land degradation and allow us to assess the impact of degradation on the evolution of land-uses.

The refinement of the revisited SALU model to enable spatial downscaling to the Groundnut Basin could be inspired by work by Rasmussen et al. (2012), and would mostly call on economic aspects. In their model of Sahelian land-use change developed on Burkina Faso, they included factors such as crop prices, outmigration, and available labour forces. According to literature dedicated to this area (Alobo and Bignebat, 2017; Diallo et al., 2022; Voss, 2022; Gangneron et al., 2024; Garambois et al., 2024), the main factors to be accounted for in a specific economic module would deal with cropping of groundnut as a cash crop and the associated monetization of agriculture, as well as the incomes associated to temporary migration to the city and to an increasing share of non-farming activities. However, implementing an economic module requires a precise analysis and quantification of migratory fluxes and economic dynamics in Senegal. For example, remittances, that is to say

the money sent by work migrants to their home community, facilitate the reconversion of rural populations to non-agricultural activities, leading to a decrease in the pressure for land. Conversely, remittances are associated with higher investment in mechanization and agricultural intensification, leading to increased yields (Lambin and Meyfroidt, 2011). If data can be acquired on outmigration from rural areas, it would thus be interesting to study their effects on predicted land-uses in Senegal. Accounting for these economic aspects would probably reduce the demand for cereal inside the Groundnut Basin itself. Additionally, to predict the evolution of land-use in the region, additional data needs to be considered to understand how fuelwood is replaced to satisfy energetic needs.

### 5. Conclusion

The implementation of our revisited version of the SALU model allowed for the reconstruction of national land-use dynamics between 1974 and 2017 in Senegal, depending on human and environmental driving factors. Results were coherent with both quantitative data from satellite imaging and known literature trends though further studies are needed to validate the model. The downscaling to regional scale however faced the limitations of the model due to demands far exceeding available space. Further research and model hypotheses are thus needed to understand the quantitative dynamics of land-uses in the Groundnut basin.

Our simulations highlight the large sensitivity of the estimated proportions of the main land-use types to human diet (in particular to cereal consumption) and to the vegetation productivity of rangelands and woody areas. This latter point is of particular importance as it should raise concerns about the specificity for land-use modeling of the Sahelian area, a densely populated area with limiting rainfall conditions for cropping and livestock raising. Future research should thus focus on improving the assessment of these variables. In addition, in order to better enable the revisited SALU model to be useful for decision-making, its ability to be spatially downscaled should also be improved, especially by enhancing our understanding of the agricultural systems in Senegal. A better knowledge of the forces driving land-use dynamics, including non-agrarian activities, will allow for further refinements of our second version of the SALU model and better modeling of the regional scale. Furthermore, at national scale, the model could be used to explore the possible land-use dynamics over the coming decades under a set of climate and economic scenarios. Having an overview of the possible trajectories might contribute to identify political leverages to reduce land-use conflicts and land degradation in the long term.

### CRediT authorship contribution statement

**Anem Dupré:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Paul-Alain Raynal:** Writing – review & editing, Supervision, Resources, Formal analysis. **Isabelle Gounand:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Caroline Pierre:** Writing – review & editing, Supervision, 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

We thank Nathalie Stéphenne for exchanging with us on the SALU model, and Luc Descroix for providing the regional rain time-series. We would also like to thank the anonymous reviewers of our paper for their helpful suggestions.

This work was supported by the Institute of Ecology and Environmental Sciences (iEES-Paris).

# Appendix A. Further details on the model

## A.1. Applicability of the model to Senegal

Both Senegal and Burkina Faso extend from the Sahelian to Sudanian Climate (Tappan et al., 2016) with precipitations ranging from 1100 to 500 mm in Burkina Faso and 1300 to 300 mm in Senegal (1991–2020 average, World Bank (2024)) with clearly defined rainy and dry seasons. Both countries experienced severe droughts during the 1970s and 1980s (Stéphenne and Lambin, 2001; Garambois et al., 2024) and increasing population pressure since the 1960s (respectively 5.1 and 3.8 million inhabitants in 1965 and 21.4 and 16.8 in 2020 for Burkina Faso and Senegal, (FAO, 2024)), with more than half the population living in rural areas. For both countries, Millet and similar cereals (Sorghum, Maize) are the main cultivated crops (Tappan et al., 2016). Finally, pastoral activities are common both in northern Burkina Faso (Rasmussen et al., 2012) and Senegal (Lericollais et al., 1999).

The main difference between Senegal and Burkina Faso lies in the evolution of cultivated land. Burkina Faso has seen one of the biggest increases in farmland in the Sahel since 1975 (>4% annual increase), while Senegal has remained stable (<1% annual increase). Groundnut cultivation, a key crop in Senegal, is also marginal in Burkina Faso (Tappan et al., 2016). The predicted country-wide cropland evolution for Senegal corresponds to the observed evolution, while the specific role of groundnut is discussed in Section 4.2.

## A.2. Between-model differences

This section presents the differences between our model and SALU, the one from which it was adapted, developed in Stéphenne and Lambin (2001).

* **Use of available time-series rather than mechanistic assumptions:**

- **Cereal yield:** in SALU, the cereal yield was originally approximated as a linear function of rainfall. The economic capacity of farmers to compensate the decreasing fallowing time then modulated this value with a degradation factor. Yet, such assumption of a linear relationship between rainfall and vegetation production was highly approximate (Fensholt and Rasmussen, 2011; Miehe et al., 2010). In our study, to bypass this limitation, the cereal yield is taken as input either from the reference database FAOSTAT (FAO, 2024) or from location-specific agronomic simulations (Brisson et al., 1998).

- **Rangeland productivity:** contrary to SALU, no explicit overgrazing was considered in our study, this notion being largely debated for the Sahelian area (see e.g. Hiernaux et al., 2016). Rangeland productivity was rather estimated from climatic data at national scale, and from climatic and soil data with a Sahelian-specific grassland model for subnational scale (Mougin et al., 1995), thus implicitly accounting for factors affecting soil fertility.

* **Sensitivity analysis:** no parameter uncertainty was taken into account in the original SALU model. In particular, the crop/past ratio parameter was set to 2/3, whereas we explore the full range of possible parameter values based on currently available data.

* **Reproducibility:** implementation was made on Stella for SALU, without a released version with calculation steps. Our model was implemented in Python and made accessible online (Dupré, 2024).

# Appendix B. Navigating the code

## B.1. Description

The simulation code is made publicly available on github (Dupré, 2024). The data used to apply the model to Senegal and the Groundnut Basin is provided in the \data folder along with a pdf description of the variables and their sources. We coded with python, using an object-oriented architecture. In the \code folder, the following files are provided:

* **model.py** The model itself is encoded in this file, with two classes used for land-use calculation: the first class is the LandUseCalculator that handles all calculations for a given year, and the second class is the LandUseModel that handles the feeding of data into the landUseCalculator for all available years and saves results so that they are easily accessible.

* **iterate_model.py** This file handles the application of the model encoded in model.py over multiple parameter sets. In particular, given the uncertainties over parameter values (Table 1), it allows for the generation of parameter sets to explore the associated differences in model outputs.

* **fetch.py** This file contains the functions needed to fetch the data inputs of our model from the \data folder. In addition, this file contains the parameter uncertainty spans described in Table 1.

* **core.py** Basic functions used for data handling.

* **sensitivity.py:** This files runs a parameter sensitivity analysis using the uncertainty spans from the fetch.py file. Both delta and Sobol frist-order indices can be calculated.

* **plot.py** This file contains the functions used to plot the model inputs and outputs, as well as the parameter sensitivity heatmap (Figs. 2, 3, 4, 5, 6).

* **MAIN.py** This file calls the functions and classes coded in the previously described files to reproduce the analysis presented in this article: the model is applied over a variety of parameter sets at national scale, the sensitivity of the model to parameter values is explored, and the model is applied at regional scale.

## B.2. Usage

If the reader is only interested in reproducing the results from this study, they could simply run the MAIN.py file. The only variable to modify in this case is the PATH_REPOSITORY which should point to the repository containing the code, data, and results folders. The plots generated will be directly stored in the results folder.

For a more advanced use of the code, the reader may want to model the land-use dynamics of a different territory or modify the parameters and inputs used for Senegal. To do so they can feed the parameter set, surface area and input time-series associated to their territory to the LandUseModel class (see Listing 1).

When faced with the problem of parameter uncertainty, the reader might want to consider launching a certain number of simulations to explore the different possible outputs (listing 2). This can be done by
<caption>Listing 1: Applying the model with a single parameter set</caption>

```python
import model
import pandas as pd

# Defining the model inputs
AREA = 19253000 # ha
PARAMS = {
    "biom_conso_min": 1.1, # tonnes/equivalent TLU
    "biom_conso_max": 2.3, # tonnes/equivalent TLU
    "food_conso": 230, # kg/inhab
    "cf": 2, # dimensionless
    "fuel_conso_rur": 0.50, # m3/inhab
    "fuel_conso_urb": 0.73, # m3/inhab
    "vegProd": 0.64, # m3/ha
    "a_biomProd": 0.005, # tonnes/ha/mm
    "b_biomProd": 0.15, # tonnes/ha
    "crop_past_ratio": 0.5 # dimensionless
}
INPUTS = pd.DataFrame({
    'year': [1999, 2000, 2001, 2002],
    'pop_rur': [220000, 120000, 250000, 640000], # inhab
    'pop_urb': [200000, 100000, 160000, 420000], # inhab
    'liv': [100000, 130000, 220000, 300000], # TLU
    'rain': [800, 820, 780, 800], # mm
    'net.imp': [100000, 110000, 130000, 100000], # tonnes
    'yield': [500, 200, 410, 500] # kg/ha
})

# Running the simulation
land_use_model = model.LandUseModel(AREA, INPUTS, params=PARAMS)
land_use_model.iterate()
print(land_use_model.lu_memory(["past", "crop", "un", "veg"]))
```

<caption>Listing 2: Applying the model with multiple parameter sets</caption>

```python
import iterate_model

NUMB_SAMPLES = 10 # number of parameter samples to draw
RANDOM_SEED = 73 # to make the choice of parameter reproducible
params_list, lu_list = iterate_model.iterate(
    NUMB_SAMPLES,
    AREA,
    RANDOM_SEED,
    INPPUTS
)
```

<caption>Listing 3: Visualizing results</caption>

```python
import plot
# Land-use dynamics of fal, crop_subs, crop_mark, un, past and veg
plot.land_uses_boxplots(lu_list, "experiment_name", AREA)
# Intensification proxys
plot.intensification_proxy(s(lu_list_nat, inputs_nat,
                             params_list)
```

calling the iterate function. This function draws random parameter values from the parameter spans defined in `fetch.py` and calculates the associated model outputs. The parameter spans can be modified in the `fetch.py` file to explore different parameter values.

The land-use dynamics of all generated simulations are contained in `lu_list`. The results can be visualized by using the functions from the `plot.py` file (listing 3).

Finally, if the reader wishes to assess the sensitivity of the model to each parameter, the `test_param_sensitivity` function can be used.

Simulations are generated based on the function inputs and the parameter ranges of the `fetch.py` function. From there, delta indices are calculated. A heatmap of delta indices can be generated by using the `sensitivity_heatmap` function of the plot file.

## Appendix C. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.ecolmodel.2025.111310.
<caption>Listing 4: Sensitivity analysis</caption>

```R
1 NUMB SIMU = 1000 # number of simulations to launch
2
3 #calculating delta indices
4 sensitivity = sensitivity.test_param_sensitivity(
5     AREA,
6     INPUTS,
7     NUMB SIMU
8 )
9 #plotting
10 plot.sensitivity_heatmap(sensitivity)
```

## Data availability

The code and data used was made available on a github repository linked in the article. Data sheets are also provided in the supplementary data.

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