# Bridging gaps in agro-ecosystem integration: a multi-level modelling study in semi-arid Burkina Faso

Gildas Géraud Comlan Assogba, David Le Berre, Myriam Adam, Etienne Delay, Erika Speelman, Katrien Descheemaeker

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Gildas Géraud Comlan Assogba, David Le Berre, Myriam Adam, Etienne Delay, Erika Speelman, et al.. Bridging gaps in agro-ecosystem integration: a multi-level modelling study in semi-arid Burkina Faso. Ecological Modelling, 2026, 514, pp.111478. 10.1016/j.ecolmodel.2026.111478. hal-05509891

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# Bridging gaps in agro-ecosystem integration: a multi-level modelling study in semi-arid Burkina Faso

Gildas G.C. Assogba<sup>a, *, e</sup>, David Berre<sup>b, c, d</sup>, Myriam Adam<sup>e, f, g</sup>, Etienne Delay<sup>h, i</sup>, Erika Speelman<sup>j</sup>, Katrien Descheemaeker<sup>a, e</sup>

## ARTICLE INFO

### Keywords:

Multi-level modelling

Model exploration

Farming system

Soil fertility

Legumes

Food security

## ABSTRACT

Mixed crop-livestock systems are central to livelihoods in semi-arid West Africa, yet they face persistent challenges related to low input efficiency, nutrient depletion, and food insecurity. These systems are characterized by complex social and biophysical interactions that produce nonlinear outcomes, complicating sustainable management.

To unravel this complexity, we developed TENGA—an agent-based model (ABM) simulating the dynamics of 700 heterogeneous farms in semi-arid Burkina Faso. The model captured spatial and temporal interactions among crop and livestock management, nutrient flows, and farmers’ interactions. A Morris sensitivity analysis was used to identify key parameters influencing system behavior, and scenario analysis explored interventions to improve crop-livestock integration including legume intensification (increasing the functional contribution of nitrogen-fixing legumes), fertilizer subsidies, and intensified direct exchange of manure and crop residues.

Nitrogen uptake efficiency, livestock feed parameters, manure management, and social interactions emerged as critical drivers of system outcomes. Sensitivity analysis revealed nonlinear feedbacks, particularly in the propagation of farm-level effects (e.g. income) to village-level dynamics (e.g. equity). Antagonistic responses among farm types reduced overall system gains, highlighting the interconnectedness of component interactions. Scenario results showed that while nitrogen-fixing legumes improved yields and reduced nutrient mining, they also reduced herd size and increased transhumance pressure. Fertilizer subsidies favored wealthier farms but did not address food insecurity among subsistence households. Intensified residue-manure exchanges enhanced nutrient recycling but had minimal impact at system level.

This study demonstrates the utility of ABMs for exploring the multi-dimensional and multi-level dynamics of mixed farming systems of semi-arid West Africa.

## 1. Introduction

Agriculture in semi-arid West Africa is dominated by subsistence farms (Giller et al., 2021b) that often combine crop cultivation with livestock rearing. Livestock feed on crop residues and produce manure

which is accumulated to later be applied to fields. Keeping livestock in addition to growing crops is also a strategy used by farmers to diversify their livelihood. However, crop-livestock integration has so far been limited because of extensive rather than intensive crop and livestock production. Indeed, the application of inputs (nutrients, water and

<footnote><sup>a</sup> Plant Production Systems Group, Wageningen University and Research, P.O. Box 430, 6700AK Wageningen, Netherlands</footnote>

<footnote><sup>b</sup> CIRAD, UPR AIDA, Bobo-Dioulasso, Burkina Faso</footnote>

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

<footnote><sup>d</sup> CIRDES-USPAE, 01 BP 454, Bobo-Dioulasso, Burkina Faso</footnote>

<footnote><sup>e</sup> CIRAD, UMR AGAP Institut, Bobo-Dioulasso 01, Burkina Faso</footnote>

<footnote><sup>f</sup> UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>g</sup> Institut National de l'Environnement et de Recherches Agricoles (INERA), Burkina Faso</footnote>

<footnote><sup>h</sup> CIRAD, UMR SENS, Montpellier, France</footnote>

<footnote><sup>i</sup> UMI UMMISCO, Ecole polytechnique, Université Cheick Anta Diop, Dakar, Sénégal</footnote>

<footnote><sup>j</sup> Laboratory of Geo-information Science and Remote Sensing, Wageningen University and Research, P.O. Box 430, 6700AK Wageningen, the Netherlands</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: gildas.assogba@wur.nl (G.G.C. Assogba).</footnote>
labor) and input use efficiency remain far from optimal mainly because of various constraints that are related to poor farmers' livelihoods (FAOSTAT, 2022; Svendsen et al., 2009; Zakari et al., 2014). The resulting yield gaps in food and feed production translate into food insecurity (van Ittersum et al., 2016) and high pressure of livestock on crop residues, which could otherwise be used for soil fertility improvement through mulch (Assogba et al., 2022; Fraval et al., 2020). Consequently, farmers heavily rely on free grazing to feed their livestock, whereas grazing areas are already under pressure because of agricultural land expansion due to poor yields and demographic growth (Herrmann et al., 2020; Ouedraogo et al., 2010). In croplands, soil fertility mining is a common problem because of the limited use of inputs in smallholder cropping systems.

Semi-arid Burkina Faso, with annual rainfall varying between 500 and 1000 mm, is a case in point of the above-described situation, with poor crop yields and livestock production, leading to food insecurity (Fraval et al., 2020; Le Garff, 2016). Intensifying crop production is a promising way for improving food security and providing sufficient feed for livestock. This relies on an increase of nutrient inputs through the application of mineral and organic fertilizer in the cropping system, resulting in soil fertility improvement (Aune and Bationo, 2008; Falconnier et al., 2023). However, in systems with limited inputs and poor livelihoods, increasing nutrient inputs is challenging and dependent on improved farm management practices (Vanlauwe et al., 2015) and/or policies aiming at reducing inputs (especially fertilizer) prices, such as subsidies (Jayne and Rashid, 2013). While subsidies can enhance access to fertilizer, the extent of crop production growth remains dependent on the socio-ecological context (Jayne et al., 2018). In terms of improved farm management, the inclusion of nitrogen (N) fixing legumes can provide supplementary nutrients through nitrogen fixation from the atmosphere. Nevertheless, the benefits depend on spatial and temporal integration of legumes in the cropping system (Franke et al., 2018).

Furthermore, intensifying crop production should go hand in hand with adequate nutrient recycling to minimize losses to the environment. In comparison to specialized systems, mixed crop-livestock systems offer better possibilities of recycling nutrients at farm and village level through interactions between farm components and farms respectively (Diarisso et al., 2015). At farm level, crop residues are fed to livestock and recycled into manure that is later applied to fields as organic input (Rufino et al., 2006). At village level, recycling of nutrients happens through interactions between farms and between farms and other landscape elements. These interactions can be direct in the form of deliberate exchange of biomass (grains, crop residues, livestock and manure) between farms (Powell et al., 2004). Besides these direct interactions, indirect interactions are linked to the social organization of the village, including collective arrangements of free grazing on crop residues considered as common resources. The latter influences livestock mobility in and out of the village and affects the amount of manure available for cropping and thus crop yields (Berre et al., 2021).

The complexity of mixed crop-livestock systems results from the diversity of management responses adopted by farmers (in the cropping systems and/or the livestock systems) when facing intensification options, as these responses are shaped by interactions among farmers and between farmers and their biophysical and socio-economic environment (Kim et al., 2022; Martin et al., 2016). In addition, changes in a complex system can lead to trade-offs between social (e.g. equity), economic (e.g. income), environmental (e.g. yields, nutrient balances) and human (e.g. food sufficiency) dimensions of sustainability. For example, Ryschawy et al. (2022) showed that interactions in the form of mutual planning of crop and livestock production as well as direct exchange of crop residues and manure between crop and livestock farmers could lead to improvement of the farm-level nitrogen balance and farmers' income but would require additional labor. Similarly, for a case study in Burkina Faso, Andrieu et al. (2015) found that crop intensification with composting led to a trade-off, between fodder self-sufficiency (farm level) and nitrogen balance of grazing areas (village level). Therefore, the

analysis of mixed crop-livestock system requires a holistic approach considering interactions between different levels (farm and village) in the farming system as well as multiple sustainability dimensions.

However, studies that explore the interplay between crop production intensification, direct and indirect farm interactions at both village and farm level in mixed crop-livestock systems are lacking. The present study addresses this gap for mixed systems of semi-arid Burkina Faso, by using scenario analysis to explore the impact of crop production intensification and farm interactions considering social, economic, environmental and human dimensions. The specific objectives were:

1. To better understand spatial and temporal interactions between farm and farming system components at farm and village level.

2. To explore synergies and/or trade-offs associated with crop production intensification options at farm and village level considering the social, economic, environmental and human dimensions.

3. To explore the impacts of direct and indirect interactions between farms at village level in the different dimensions.

We used an agent-based model (ABM), as it allows a spatially explicit representation of the study area as well as the representation of individual farms and their decision-making process. Indeed, ABMs are useful in studying spatial and social interactions in complex systems such as farming systems (Grillot et al., 2018). It is a bottom-up modelling approach, built from individual entities, such as farms, and their interactions to better understand collectives, such as the farming system, and the emergent properties of the system at the larger village or landscape level. In semi-arid West Africa, ABMs have been used to investigate various aspects of farming systems, such as nutrient cycling and landscape changes (Grillot et al., 2018), carbon dynamics (Belem et al., 2011; Scriban, 2024) climate variability and crop varietal diversity (Belem et al., 2018), and mulching (Berre et al., 2021). However, to the best of our knowledge, no study investigated the effects of crop production intensification and farm interactions on biomass production and flows and the subsequent impacts on social, economic, environmental and human dimensions of sustainability at farm and village level (Table S1, supplementary material). As ABMs are often complex models, testing the robustness of their outputs through sensitivity analysis is key to build trust and better understand processes leading to their outputs (Borgonovo et al., 2022). We performed a model sensitivity analysis with all the input parameters to assess the model's robustness.

## 2. Materials and methods

The Sections 2.2, 2.3 and 2.4 describe the theoretical framework of TENGA (Tool for the Evaluation of Nutrient flows and Governance in Agroecosystems) according to the ODD (Overview, Design concepts and Details) protocol proposed by Grimm et al. (2010). TENGA is both an acronym for our model and a word meaning “land” in Mooré, the most widely spoken language in Burkina Faso. TENGA was developed with NetLogo version 6.2.1 (Wilensky, 1999) and sensitivity analysis was performed using the OpenMole platform (Reuillon et al., 2013). Details about the model exploration are described in Section 2.4.2.5.

### 2.1. Case study description

The study area is the village of Yilou (13.02°N; 1.55°W), located in semi-arid Burkina Faso in the province of Bam (Fig. 1). The village has an area of 35 km². The climate is characterized by one rainy season from June to October. The annual rainfall ranges from 452 mm to 1157 mm, with an average of 676 mm in the 1964–2019 period. Sorghum and cowpea are the most widely cultivated crops in the village. Sorghum is often intercropped with cowpea using the traditional intra-hill intercropping method, while crop rotation in the study area is rare (Ganeme et al., 2021). Five types of land use were identified in Yilou by Dione (2020): 51 % of the area is covered by agriculture, 33 % bare soil, 13 %
<figure><img src="image_4.png" /><figcaption>Fig. 1. Location map of study area (Yilou) in Burkina Faso and Africa, showing the different land use types.</figcaption></figure>

woodland (considered as pastureland), 2 % settlement and 1 % water (Nakambe River). The village is crossed by a national road which facilitates households' access to a market and the village connection to surrounding cities. Many households of the village are engaged in gold mining which represents an important source of off-farm revenue. The farming system in Yilou is characterized by four farm types (Assogba et al., 2022) (Table 1), including two subsistence farm types: Subsistence-Oriented Crop (SOC) and Subsistence-Oriented Livestock (SOL) farms. The third type is the Market-Oriented and Diversified (MOD) type. This type had the largest cultivated area, a larger herd size than SOC and SOL, and half of their total revenue coming from off-farm activities (e.g. commercial shops and gold mining). Lastly, the Land Constrained Livestock (LCL) type was characterized by the smallest cultivated area, and by far the largest herd size dominated by cattle (90 % of the herd). LCL farms were the only farm type practicing transhumance i.e. migrating southward with their animals to get more forage in period of feed scarcity. The proportions of each farm type in Yilou

were 36 % SOC, 32 % SOL, 26 % MOD and 6 % LCL.

## 2.2. Overview of the model

In this section, we will describe our agent-based model TENGA using the ODD framework (Grimm et al., 2010, 2020). The ODD protocol is a standardized framework for describing ABMs in a structured and transparent way. It ensures clarity, reproducibility, and comparability across models.

### 2.2.1. Purpose

The purpose of TENGA is to explore the impact of interactions between farms and farm components and resource allocation (land and nutrients) to crops on biomass (crop and livestock) production and flows, household food sufficiency and income, as well as nutrient use efficiency and balances at farm and village level in semi-arid Burkina Faso. Details on the model parameters and outputs are available in respectively Table S1 and Table S2 in supplementary material. The observed patterns generated by the model include: crop yields and food sufficiency distribution as influenced by rainfall and nutrients availability, livestock mobility outside the village, the reliance on external feed (from fields and pastureland) as influenced by livestock mobility and crop residues production and management, soil fertility management status and trend in cropping systems and pastureland as reflected by values and trends of nutrient balance and nutrient use efficiency. The detailed list of indicators used to discuss (4.2) the suitability of TENGA regarding observed patterns is listed in Table S1.

### 2.2.2. Entities, state variables and levels

#### 2.2.2.1. Entities.
TENGA comprises entities (i.e. agents) that interact with their environment. Two types of entities were included in the model: farmers and livestock. Each farmer represents a household and manages one farm, consisting of fields, crops, livestock and money. Indeed, in the model, each farmer grows crops (defined at initialization - 2.4.1 and depending on scenarios - 2.4.2.4), harvests grain and crop

**Table 1**

Mean and standard deviation of characteristics of farm types in the study area. TLU = Tropical Livestock Unit. 1 FCFA = 0.0015 EUR. Farm types include LCL = land constrained livestock; MOD = market-oriented and diversified; SOC = subsistence-oriented crop; SOL = subsistence-oriented livestock. Data from Assogba et al. (2022).

<table><thead><tr><th>Variables</th><th>SOC</th><th>SOL</th><th>MOD</th><th>LCL</th></tr></thead><tbody><tr><td>Household size (persons)</td><td>8 ± 4</td><td>7 ± 3</td><td>12 ± 4</td><td>15 ± 9</td></tr><tr><td>Herd size (TLU)</td><td>3 ± 2</td><td>3 ± 4</td><td>5 ± 4</td><td>32 ± 14</td></tr><tr><td>Cultivated area (ha)</td><td>2.3 ± 1.1</td><td>1.7 ± 0.9</td><td>3.6 ± 1.6</td><td>1.5 ± 0.7</td></tr><tr><td>Share of cultivated area rented in (%)</td><td>7 ± 24</td><td>19 ± 36</td><td>3 ± 14</td><td>50 ± 53</td></tr><tr><td>Share of cattle in herd (%)</td><td>30 ± 30</td><td>20 ± 30</td><td>40 ± 30</td><td>90 ± 10</td></tr><tr><td>Total annual income (1000 FCFA)</td><td>94 ± 146</td><td>83 ± 127</td><td>957 ± 131</td><td>612 ± 497</td></tr><tr><td>Contribution of off-farm revenue to total income (%)</td><td>30 ± 30</td><td>20 ± 30</td><td>50 ± 30</td><td>10 ± 20</td></tr></tbody></table>
residues to feed his/her household and livestock respectively. In addition, each farmer can sell and buy livestock and also manages livestock mobility across and outside the village. Farmers earn income from crop and livestock selling as well as off-farm activities. The income is used to take care (e.g. health care, schooling) of the household and purchase inputs (e.g. fertilizer and crop residues). Farmers can exchange biomass (crop grain and residues as well as manure) with other farmers according to their needs. For example a crop-oriented farmer can exchange its surplus of crop residues with a pastoralist who provides manure in return.

Three types of livestock were included in TENGA: cattle (bulls and cows), donkeys and small ruminants (representing both goats and sheep). Each of them can move, eat, reproduce, and produce manure according to its type (see Section 2.3.1). The aggregation of all livestock belonging to a certain farm represents a herd. A herd is managed by one farmer. More details about the entities' behavior can be found in Section 2.3.3.

### 2.2.2.2. Environment.
The environment was based on the actual land use map of Yilou. There were five types of land use in the environment: agriculture, bush, bare soil, water and settlement. The environment was made up of plots which constitute a regular grid of 30 m x 30 m resolution (approximated to 0.1 ha). A field was made up of one or more adjacent plots. The average field area in the model was 0.4 ha (4 plots), reflecting our field observations in Yilou (Assogba et al., 2023). Fields in the model were the closest possible to their owner settlement based on empirical observations (Assogba et al., 2023).

### 2.2.2.3. State variables.
The class diagram presenting operations and state variables of each entity and the environment of TENGA is presented in Figure S1, supplementary material.

### 2.2.2.4. Temporal level.
The time step of the model is one day and indicators (e.g. nitrogen balance) were calculated on a yearly (365 days) basis. The time step of the model was set to reflect daily livestock mobility, feeding and manure production as well as farmers' decisions regarding biomass (mainly grain and crop residues) and livestock. The first step in the model corresponds to the harvest. Indeed, after initializing all state variables of the model, grain and crop residue production are computed according to rainfall and nitrogen application of each farm (see 2.3.1 for more details). Therefore, biomass (grain, crop residues harvested and left on the soil in fields) is made available to all farms from the beginning of the simulation. It was assumed all crops are harvested at the same time.

#### 2.2.3. Process overview and scheduling

The ensemble of operations taking place during a day (one time step) in TENGA are presented in Box 1 using pseudo-code.

### 2.3. Design concepts

#### 2.3.1. Basic principles

##### 2.3.1.1. Crop and grass production.
The crop grain and residue yields were calculated from multiple linear regression equations derived from previously calibrated crop growth models in DSSAT (Cheriere et al., 2025; Hoogenboom et al., 2019). The regressions equations were used to simplify yield and crop residues estimation as function of the main limiting factors in the study area: nitrogen and rainfall (Diarisso et al., 2016; Sanou et al., 2023). Sorghum (local variety CSM63E), cowpea (variety TVU3644 from Niger) and groundnut (variety Chinese from Ghana) grain and crop residue yields were simulated in sole cropping for various N application rates from manure and mineral fertilizer (NPK). The traditional recommended N application rate for sorghum was 37 kg N/ha (Ikazaki et al., 2024). Amounts of N applied in the crop model ranged from 0 to 40 kg N/ha (with a 10 kg N/ha increase) for manure and mineral fertilizer each. The combination of manure and mineral fertilizer followed a full factorial design. More details concerning the crop modelling can be found in Tables S3 and S4. The simulations were run for 32 years (1990 to 2021) for which we had rainfall data from the Ouagadougou weather station (100 km away from Yilou, and similar climate zone). The outputs (grain yield, total aboveground biomass, crop nitrogen uptake and soil nitrogen mineralized) of the crop model for the different levels of N applied each year were used in a multiple regression analysis (Table S5). Crop residue production was calculated by subtracting the grain yield from the total aboveground biomass produced by a crop. Based on MRSI (2014), the maximum achievable grain yield in TENGA was 2.5 t/ha, 1.2 t/ha and 1.5 t/ha respectively for sorghum, cowpea and groundnut. Likewise, the maximum crop residues production in the model was 5.8 t/ha, 2.8 t/ha and 3.5 t/ha respectively for sorghum, cowpea and groundnut given a harvest index of 0.3 for the three crops (Ganeme, 2022). In case the predicted yields from the linear regressions exceeded potential yields, they were automatically reset to the potential yields.

The amount of fertilizer and manure applied by each farm depends on its income, herd size and the number of days spent by animals within the village. As a simplification, N from roots and crop residues of the preceding cropping season was added to N from manure in the regression equations (Table S5), except for groundnut which was harvested with all its root biomass. Similar N content was used for crop residues and roots (www feedipedia.org, accessed on 01/06/2022). Manure was assumed to contain 1.3 % of N (Sileshi et al., 2017). The N content (23 %) of fertilizer (NPK) purchased in Yilou was based on direct observations from labels on the fertilizer bags.

The effect of cereal-legume rotation was implemented in TENGA by considering only the N effect on the soil and subsequent crops. In fact, the N input from decomposing roots of cereals and legumes of the preceding cropping season was considered as N input for the current crop. In the rotation, sorghum is grown on all plots previously grown with legumes and vice versa. The plots in which sorghum and legumes are rotated were chosen randomly and the proportion of cropland was based

### Box 1

Process scheduling of the model.

If Day Year = 1 then

Set up(): load environment, create entities and update state variables of entities and plots

Cropsim(): compute crop (grain and crop residues) production as well as carbon balance and nitrogen balance and use efficiency

Bushim(): compute grassland production

Livsim(): compute livestock movement, eating, weight gain/loss, reproduction and manure production

Biomanag(): compute household and biomass management by farmers in the village

Next step: Day Year = Day Year + 1

If Day Year > 365 then

Export(): write model's outputs into a csv file

Day Year = 1

Year = Year + 1
on observed data per farm type (Table 2).

In addition to crop rotation, the traditional intra-hill sorghum cowpea intercropping (SCT) was also implemented in TENGA. We considered that sorghum and cowpea occupied 83 % and 17 % of the cultivated area respectively in this intercropping type (Ganeme et al., 2021). In pure stand, sorghum rows were 80 cm apart and on each row, 40 cm separated two hills. The same spacing was respected in the traditional intra-hill intercropping. The partial and total Land Equivalent Ratio (LER) of each intercropping type were derived from (Ganeme, 2022) and are presented in Table S6. In intercropping, grain and crop residue yields were first calculated in pure stand using the equations in Table S5 and then multiplied with the partial LER values in Table S6.

Grass production in pastureland was derived from Breman and de Wit (1983) according to the total amount of rainfall in a given rainy season. Indeed, the annual grass production was determined by linear interpolation between 400 mm (equivalent to 1.5 t DM/ha of grass) and 1000 mm (equivalent to 4 t DM/ha of grass) of annual rainfall.

### 2.3.1.2. Livestock production.
Livestock feed requirement (energy) was calculated from AFRC (1993) (see eq. (1), eq. (2) and eq. (3)). Coefficients in the equations were calculated considering that the distance walked daily by each animal was 12 km (Zampaligre and Schlecht, 2018). Daily dry matter intake (when grazing) and manure production were a function of the animal weight as found by (Assouma et al., 2018). The daily dry matter intake of livestock while grazing (grass and crop residues) was equal to 73 g DM/LW<sup>0.75</sup> (LW = Live Weight), in case sufficient feed was available. The total daily energy intake of livestock is the sum of energy provided by its owner (2.3.3) and energy consumed from grass/crop residues when grazing. The daily manure production was set to 2.1 kg DM (Dry Matter)/TLU (Tropical Livestock Unit, 1 TLU = 250 kg). Livestock were assumed to spend 8.5 (cattle and donkey) and 9 h (small ruminants) grazing per day (Zampaligre and Schlecht, 2018) and the remaining hours in the shed at home. Hence, the fraction of produced manure accumulated near the homestead was 0.62 (small ruminants) and 0.64 (cattle and donkey) (Ayantunde et al., 2001). In addition, 55 % of the total N excreted in the shed was assumed to be lost due to poor manure management (Rufino et al., 2007).

#### Calculation of metabolizable energy requirement (MJ/day)

##### Cattle and donkey

$$ME = \frac{0.53 \left( \frac{W}{1.08} \right)^{0.67} + 0.037W}{k_m} \quad (1)$$

##### Small ruminants

$$ME = \frac{0.315W^{0.75} + 0.048W}{k_m} \quad (2)$$

$$k_m = 0.35 \left( \frac{ME_f}{GE_f} \right) + 0.503 \quad (3)$$

Where: W is the weight of the animal in kg, which we assumed to be constant throughout the year. We assumed the following: 1 cattle = 0.7 TLU, 1 donkey = 0.5 TLU and 1 small ruminant = 0.1 TLU, where TLU stands for Tropical Livestock Unit and 1 TLU = 250 kg (Le Houerou and

<caption>**Table 2**</caption>

Land allocation to crops per farm type. For cowpea and groundnut, the data correspond to the share of land in pure stand whereas for sorghum the share of land included intercropped (traditional intra-hill) fields. Data from Assogba et al. (2022).

<table><thead><tr><th>Farm types</th><th>Sorghum (%)</th><th>Cowpea (%)</th><th>Groundnut (%)</th></tr></thead><tbody><tr><td>SOC</td><td>72</td><td>13</td><td>15</td></tr><tr><td>SOL</td><td>70</td><td>15</td><td>15</td></tr><tr><td>MOD</td><td>69</td><td>17</td><td>14</td></tr><tr><td>LCL</td><td>82</td><td>14</td><td>4</td></tr></tbody></table>

Hoste, 1977). ME is the metabolizable energy required for maintenance by the livestock in Mega Joules per day (MJ/day). $k_m$ is the efficiency of utilization of metabolizable energy in feed by livestock. $ME_f$ and $GE_f$ are respectively the metabolizable energy and gross energy contents of feed in MJ/kg of dry matter. The metabolizable and gross energy content of feed were retrieved from https://www feedipedia.org/ (accessed on 01/06/2022).

### 2.3.2. Emergence

The spatial distribution of the nitrogen balance and nitrogen use efficiency values emerged at village level. The spatial distribution of nitrogen balance and nitrogen use efficiency were the result of nutrient management at individual farm level and livestock grazing and manure dropping in grazing areas (fields and pastureland) within the village.

### 2.3.3. Agents' behavior

Biomass to feed the livestock was scarce in Yilou (Assogba et al., 2023). In TENGA, farmers addressed the lack of crop residues by purchasing the amount of crop residues required until the end of the dry season and/or by sending part of their cattle out of the village (see farmers' activity diagram, Figure S2). Every purchase in the model was supposed to take place at the market which was assumed to have unlimited resources. Although farmers in the model did not know the energy requirements of their animals, they judged whether the life of a particular animal was at stake based on the feed level (eq. (4)) of the animal. An animal was considered starving if for >10 days its feed intake was <40 % of the energy requirement. As livestock in semi-arid West Africa are able to cope with forage scarcity by adjusting their feed intake and body weight (Assouma et al., 2018; Ickowicz and Moulin, 2022), we assumed in the model that animals could survive with 40 % of their energy requirement (Assouma et al., 2018). However, the impact of feed quantity on livestock weight was not considered in the model. The amount of crop residues (harvested and purchased) provided daily to livestock was derived from biomass monitoring data (Assogba et al., 2023). Farmers also collected the manure excreted by their livestock near the homestead on a daily basis. When out of the village, livestock was assumed to feed only through grazing, and manure was left out of the simulated system.

$$Feedlevel = \frac{Dfeed}{Feedreq} \quad (4)$$

Where Dfeed is the daily feed (energy) consumed by an animal in Mega Joules of Metabolizable Energy (MJ ME). Dfeed is the sum of energy provided to livestock by its owner plus energy consumed when grazing. Energy consumed when grazing is 73 g DM/LW<sup>0.75</sup> (LW = Live Weight) (Assouma et al., 2018). Feedreq is the daily energy requirement for maintenance of an animal in MJ ME calculated based on Eq. (1), (2) and (3).

After harvest, livestock fed on crop residues left on the soil in fields. Animals also grazed on grass available in pasture during the dry season (from harvest to the onset of the rainy season) and the rainy season. Farmers in the model were assumed to know the distribution of crop residues and grass in the village. The potential grazing area of each farmer was located in a radius of 6 km around the homestead. In the model, the potential grazing area was divided into four equal quadrants. Each farmer sends his animals for grazing on the quadrant which has on average the largest amount of biomass (crop residues and grass) available. The grazing animals were distributed over all the plots in a quadrant in such a way that the number of animals per plot is minimized. When there is no more grass and crop residues left, livestock stayed in the shed at home or may be sent out of the village by the owner (LCL farms only) (Figure S2). We assumed that female animals reproduce when for a one-year period their feed level was equal or greater than 0.8. Farmers sell livestock to get income and purchase grains and crop residues or more livestock to increase their herd size. These
patterns were simulated according to observations per farm type, described in more detail in Assogba et al. (2022). Prices of items (e.g.: grains of each crop, livestock and fertilizer) were based on local observations and presented in Table S7.

### 2.3.4. Objectives

Farmers' decision-making was designed to meet household needs, possibly help non-food-sufficient households (see 2.3.7), increase their crop and livestock production and thereby their food sufficiency and on-farm income. Concerning livestock, farmers' objective was to meet their energy requirement so that they can survive and eventually reproduce (2.3.3).

### 2.3.5. Sensing

Farmers in the model know the different types of land use. They are aware of the spatial availability of crop residues, forage and manure (in pastureland) in the village. They are also aware of their neighbors and livestock grazing in the village. They can identify their livestock and fields and locate their houses. Farmers send their livestock for grazing on plots of the village that are closest to their settlement with maximum forage and/or crop residues available. Livestock from different farms can graze on the same plot.

The livestock can perceive each other and the amount of forage available on the plot they are on.

### 2.3.6. Prediction

Each farmer keeps in memory the amount of feed (metabolizable energy) harvested and purchased, the herd size (TLU) and the amount of manure collected (kg of dry matter). Only the values of the five last years during simulation are stored and updated continuously. Based on their memory, farmers predict:

(1) the amount of feed to purchase as a function of feed harvested and herd size. The predicted amount of feed to purchase is transformed into kg of dry matter assuming only sorghum residue was purchased.

(2) the amount of manure to collect from homestead as function of herd size.

The predicted values are used in exchange of crop residues and manure between farms (2.3.7).

### 2.3.7. Interactions

Farmers can interact directly with each other through exchange of grain (2.3.3), crop residues and manure. Farmers who predicted right after harvest that they will have a surplus of feed (crop residues) can exchange the predicted surplus with farmers who predicted they will have a deficit of crop residues. Each agent can send and receive crop residues/manure to/from one or several other farmers. Farmers who predicted they would need additional crop residues could not receive more crop residues than their predicted manure production. Crops residues and manure were exchanged on a 1 kg – 1 kg basis. At the end of the dry season, farmers who received crop residues had to send back an amount of manure corresponding to the amount of crop residues received. If the amount of manure they collected is inferior to the amount of crop residues received they will have to send the remaining manure the next year, before applying it to their own plots.

In addition, farmers interacted directly through exchange of grains between food-sufficient and non-food-sufficient farmers. We assumed that farmers, after having met their household needs, sell 80 % of their surplus grains each year (Figure S2). The remaining 20 % was stored and can be used the following years. The daily energy requirement of households was assumed at 2250 kcal per adult equivalent (AE) (FAO et al., 2001). Farmers who could not meet their households' needs with their grain production (non-food-sufficient farmers), purchased the amount of food required to meet their needs if they have sufficient

income. If not, they asked for help from the four closest neighbors. This was based on the observation that some farmers who were not food sufficient could receive grains as aid from food sufficient farms (Assogba et al., 2023). Based on our observations, grain was given among related farms often living in the same area of a village. We assumed that neighbors with excess production would provide non-food-sufficient farms with 10 % of their stored grain. For each non-food-sufficient farm, this help was received only once in a year in the simulation. The order in which non-food-sufficient farms received aid was random. If the help and/or the food purchased for a given year was not enough, farmers with less food than required are considered non-food-sufficient for the year.

Farmers in the model also interacted indirectly through livestock free grazing on crop residues left on the soil in fields. Indeed, the model simulated different crop residue management options for each farm type, i.e. the fraction of crop residues produced to be harvested or left on the soil, was based on field observations described in (Assogba et al., 2022).

### 2.3.8. Stochasticity

At initialization, farmers' resources (e.g. cultivated area and herd size) were generated based on typology data (Table 1; (Assogba et al., 2022)). For each resource, farmers were attributed a random value in the interval of the mean plus or minus the standard deviation according to their farm type. The distribution of cultivated fields was random, as to our knowledge there was no particular spatial distribution of fields in Yilou. The sex of donkeys and small ruminants (including newborns) was also random, with each sex having 50 % of chance of occurrence. The same applied for cattle of large livestock owners. However, for subsistence and market-oriented farms, the sex of cattle was set to male (see Section 2.1 for more detail on farm types) based on our observations in the farming system. In the study area cattle were primarily kept as draught animals and/or for fattening and not for milk production. The amount of annual rainfall in the model was randomly chosen from an interval ranging from 450 mm to 1200 mm, based on historical data. A rainfall series of 20 consecutive years was generated and used in all simulations. The average rainfall generated was 703 mm, the minimum and maximum were 480 and 880 mm respectively.

### 2.3.9. Collectives

The aggregation of cattle, donkey and small ruminants of each farm is considered as the herd. Animals of each herd grazed on the same grazing area defined by the farmer (2.3.3).

## 2.4. Details

### 2.4.1. Initialization

TENGA was set up with a village of 700 different farms in the simulation. The number of farms in the village was based on data from Assogba et al. (2022). Based on our knowledge of the farming system, there was no particular spatial distribution of farm types, so that farms in the model were distributed randomly across the villages. All cultivated plots were assigned random nitrogen (N) and carbon (C) content values that range between the mean plus or minus the standard deviation of measured N and C content of 38 plots in the study area (Assogba et al., 2023). Manure accumulated by each farm was calculated based on their herd size. The amount of fertilizer applied by each farm type was based on data from Assogba et al. (2022). All farms were assumed to apply all the manure they collected from their livestock.

### 2.4.2. Analysis

#### 2.4.2.1. Partial nitrogen balance and nitrogen use efficiency.
Nitrogen (N) is the most limiting nutrient in cereal-based systems of West Africa (Adam et al., 2020; Falconnier et al., 2020). Therefore, our nutrient
analysis focused on N in this study. The partial nitrogen (N) balance and nitrogen use efficiency (NUE) were calculated for the cropping system (Eq. (5) to Eq. (8)) at farm and village level, whereas only the N balance was calculated for the pastureland (Eq. (9)) at village level.

N input in the cropping system of a farm (village) was the sum of N from roots and crop residues used as mulch in the previous cropping season and N from manure and mineral fertilizer applied to all fields cultivated of the farm (village). In addition, input from biological nitrogen fixation of legumes (cowpea and groundnut) was considered. A root:shoot ratio (root biomass divided by shoot biomass) of 0.3 was considered for all crops (Anderson, 1988). N content of crop residues were derived from https://www feedipedia.org/ (accessed on 01/06/2022) by dividing their Crude Protein (CP) content by 6.25. The amount of N fixed by cowpea and groundnut was estimated as 64 % and 70 % of the total yield (crop residues and grain) (Phoomthaisong et al., 2003; Sanginga, 2003). N output was the sum of nitrogen in crop residues and grains harvested and grazed in all fields of a farm (village).

N inputs in pastureland included N from grass roots and shoots not grazed of the previous year and N from manure deposits from grazing animals. A root: shoot ratio of 0.3 was also considered for grass. The N output was the N contained in the total amount of grass eaten by livestock. Because biomass production in pastureland was a function of only the total rainfall, nitrogen use efficiency was not calculated for pastureland.

Equations

$$ \text{Nbal}_{\text{csf}} = \frac{\sum_{i=1}^{n} (\text{Input}_i - \text{Output}_i)}{F_T} \qquad (5) $$

$$ \text{NUE}_{\text{csf}} = \frac{\sum_{i=1}^{n} \text{Output}_i}{\sum_{i=1}^{n} \text{Input}_i} \qquad (6) $$

$$ \text{Nbal}_{\text{csv}} = \frac{\sum_{i=1}^{m} (\text{Input}_i - \text{Output}_i)}{V_T} \qquad (7) $$

$$ \text{NUE}_{\text{csv}} = \frac{\sum_{i=1}^{m} \text{Output}_i}{\sum_{i=1}^{m} \text{Input}_i} \qquad (8) $$

$$ \text{Nbal}_{\text{pl}} = \frac{\sum_{i=1}^{k} (\text{Input}_i - \text{Output}_i)}{P_T} \qquad (9) $$

Where Nbalpl, Nbalcsf, and Nbalcsv, are respectively the nitrogen balance of pastureland and the cropping system at farm and village level in kg/ha. NUEcsf and NUEcsv are respectively the nitrogen use efficiency at farm and village level in kg N/kg N. Inputi and Outputi are respectively the nitrogen input and output in the ith field of a farm/village or the ith pastureland plot of the village, in kg N. PT, FT and VT are respectively the total area of pastureland, the total area cultivated by a farm and in the village, in ha. n is the total number of cultivated fields owned by a farm whereas m and k are respectively the total number of cultivated fields and the total number of pastureland plots in the village.

### 2.4.2.2. Household food sufficiency and income distribution.
Household food sufficiency was calculated by dividing the households' energy available (from own production, purchases from the market and gifts from the neighbors) by the amount of energy required. The energy content of food was retrieved from the U.S. Department of Agriculture database (https://fdc.nal.usda.gov/index.html, accessed on 15/06/2022). Food sufficiency in the village was calculated by dividing the total energy available in all households in the village by the total energy

required by all households in the village. The total income of the farm was the sum of income from selling crop and livestock and income from off-farm activities. Off-farm income was added to the total income once each year based on the farm type (Table 1). The total income in the village was the sum of total income of all farms. The average total income per farm was calculated by dividing the village level total income by the number of farms in the village.

Equity in income distribution of farms in the village was assessed using the Gini index (Gastwirth, 1972). The value of the index varies from zero to one with zero meaning a perfectly equal distribution of income and one meaning complete inequality (i.e. one person owns all income in the village).

### 2.4.2.3. Biomass production and flows.
Biomass production and flows (inflow and outflow) at farm and village level were calculated. Biomass inflow at farm level included biomass purchased and received as aid and biomass grazed on other farmers' fields. Biomass outflow at farm level included biomass sold, given away and livestock death. Biomass inflow at village level included the total amount of biomass purchased by households in the village. Indeed, it was assumed that, given the context of biomass scarcity, biomass available on the market came from outside the village. Also, it was assumed that all biomass sold by farmers on the market was leaving the village, so an outflow. In addition, manure lost when livestock are grazing outside of the village was also considered as an outflow.

### 2.4.2.4. Baseline scenario.
The model was first run based on our knowledge of the current farm management practices, described in more detail in Assogba et al. (2022) and Assogba et al. (2023). Land allocation to crops for each farm type is presented in Table 2. All farm types were assumed to grow half of their sorghum plots in intra-hill intercropping with cowpea. Manure and fertilizer were only applied to sorghum fields. Cowpea in intercropping with sorghum received the fertilization with sorghum. As a simplification, no crop rotation was simulated in the baseline scenario as to our knowledge crop rotation was rarely practiced by farmers in the study area (Ganeme et al., 2021). The exchange of manure and crop residues was also not considered in the baseline. However, exchange of grains between food-sufficient and non-food-sufficient farms was included in the baseline. Only sorghum residues were left on the soil after harvest, while all legume residues were harvested. The proportion of sorghum residues left on the soil for grazing was 71 %, 75 %, 84 % and 89 % for SOC, SOL, MOD and LCL farms respectively (Assogba et al., 2022). Because of stochasticity, the model was run 50 times with the same values for the input variables and the average and standard deviation of each indicator were calculated for the 20 years of the simulation and 50 model runs. After 50 runs there was no significant variation in the average value of all indicators (3.1).

### 2.4.2.5. Sensitivity analysis.
We performed a sensitivity analysis using the Morris approach (Campolongo et al., 2011). The Morris sensitivity analysis is a one factor at time (OFAT) method that assesses the impact of variation in each parameter on the model outputs, called absolute elementary effects (Campolongo et al., 2011). The Morris approach has the advantage to be computationally cheap while performing a robust sampling of the parameter space. In order to compare the effect of parameter variations on all outputs, the proportion of change (PC) was calculated (Eq. 10). In addition, the coefficient of variation of the elementary effects was also computed. This allowed to analyze the variability and direction of change (positive/negative) related to the elementary effects of each parameter. A relatively high coefficient of variation implied that the effect of the parameter was non-linear i.e. its variation led to both high/positive and low/negative output responses. A positive/negative coefficient of variation meant that a parameter had on average a positive/negative effect on a given output.
$$PC = \frac{AEE * 100}{ABV} \qquad (10)$$

Where: PC = proportion of change. AEE = average absolute value of elementary effects from varying a parameter on a given output, the average is calculated across model runs. ABV = average absolute value of the output from the baseline scenario.

We varied (increased and decreased) the value of each of the 38 parameters (Table S2) of the model with 25 % of its actual value. For a given value of a parameter, the model was run 10 times to take stochasticity into account. In total 4800 model runs were executed. The number of runs and parameters included in the sensitivity analysis were chosen as the best trade-off between computation power (and time) and accuracy of the sensitivity analysis.

### 2.4.2.6. Alternative scenarios.
In addition to the baseline scenario, seven other scenarios were tested to explore the impact of crop diversification, increased nutrients inputs and farms interactions on the farming system:

(1) The baseline exchange scenario. The only difference with the baseline scenario was the possibility of direct exchange of crop residues for manure (and vice versa) (2.3.7). With this scenario, we explored whether it was possible, given the current farm management, to improve nutrient recycling at village level.

(2) The diversification scenario. This scenario combined crop diversification (intercropping and rotation) and organic matter management. The traditional intercropping practiced by farmers was extended to 100 % of sorghum fields. In addition, we also simulated a 2-year rotation between sorghum and legumes (cowpea and groundnut). For subsistence farms (SOC and SOL) only, who had the smallest herd size, 100 % of the legume residues was incorporated in the soil as organic amendment. MOD and LCL farms harvested 100 % of their crop residues and used it to feed their larger livestock herds.

(3) The high-intensification and fertilizer50 scenarios. The high intensification scenario built on the diversification scenario and assumed a subsidy policy, reducing fertilizer prices by 25 %. The fertilizer50 scenario was similar to the baseline scenario, but assumed a 50 % reduction of the fertilizer price. In these scenarios we assumed that a lower price would encourage farmers to purchase fertilizers and increase the nutrient application rates.

(4) The diversification, high-intensification and fertilizer50 exchange scenarios built on the previous scenarios and included the possibility to exchange crop residues and manure in the village.

For all scenarios, performance indicators were calculated and compared based on the average of the last 15 years of simulations. The first five years were excluded as direct exchange between farms only started after the fifth year.

## 3. Results

### 3.1. Baseline scenario

Simulated crop yields remained stable at both farm and village levels throughout the 20 years of the simulation (Fig. 2a). In TENGA, crop yields were mainly determined by nitrogen uptake (and thereby nitrogen input) followed by rainfall (Table S5). As nitrogen application in the cropping system was generally low (34 kg N/ha on average) simulated crop yields were related to rainfall (Fig. 2a). The only exception were LCL farms who applied on average 73 kg N/ha and obtained much better sorghum and cowpea yields than the other farm types. Groundnut was not intercropped with sorghum and did not receive any fertilization so that yields were similar across farm types. The total amount of feed provided per TLU was lowest for SOL and LCL farms and similar across other farm types and at village level. Crop residues harvested contributed 64 % of livestock feed in the village whereas forage grazed in pastureland within and outside of the village contributed 24 %. Purchased crop residues had contributed only to 12 % of livestock feed.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Grain yields (a) and annual crop residues and forage consumption per TLU, including crop residues harvested (and purchased) (b) at village and farm level for the baseline scenario. Cowpea yields include yields in intercropping with sorghum. DM = dry matter. CR = crop residues. TLU = tropical livestock unit. LCL = land constrained livestock; MOD = market-oriented and diversified; SOC = subsistence-oriented crop; SOL = subsistence-oriented livestock. Error bars are standard deviation from 50 runs of the model.</figcaption></figure>
Moreover, on average the annual livestock feed requirement was never met in the village as ingested feed by livestock represented only 53 % of their requirement. In contrast with the village level, the contribution of forage from pastureland was much lower (5 %) for all farm types except LCL, who relied mostly (71 %) on grazing in pastureland in and outside the village. The contribution of grazing on crop residues left on fields to the total amount of feed was negligible at farm level. In addition, the amount of crop residues harvested and purchased per TLU decreased over time from the initial value by 64 % and 32 % for SOC and SOL farms respectively (Fig. 2b). This decline was due to the increase of their herd size (Fig. 3a) while yields remained stable. For LCL farms, the herd size first increased from 36 TLU to 43 TLU and later stabilized around 32 TLU. The herd size of MOD farms slightly increased throughout the years from 6 to 8 TLU. The amount of manure collected at farm level followed a similar trend as herd size. For the LCL farms, the amount of manure was also influenced by the number of days after harvest that the herd had to leave the village due to feed scarcity (transhumance) (Fig. 3b). Transhumance started on average on day 220 (Fig. 3c) corresponding to 20 days before the start of the cropping season.

The positive trend in herd size can be linked to an increase in the total income of farmers in the model, except for LCL farms (Fig. 3d). The increase of income at farm level was also noticed at village level. The Gini index calculated on the total income remained stable throughout the simulation and was on average 0.60 (Standard Deviation = 0.05). This suggested that the village experienced a strong inequality in the distribution of the total annual income which was earned mainly by a few (MOD) farms.

Food sufficiency was always reached at the village level (113 % on average) (Fig. 3e) and was strongly correlated to rainfall (Pearson correlation = 0.68, p-value < 0.01). However, SOL farms could often not reach food sufficiency, and 8 % of them were not food sufficient on average. At village level, yearly food purchases were necessary to reach food sufficiency with an average of 16 kg grain purchased / adult

equivalent (AE) (data not shown). At farm level, with the exception of MOD farms owning the largest cultivated area, all farm types purchased additional grains. LCL and SOC farms purchased the least (15 kg/AE) whereas SOL farms purchased the most (28 kg/AE). Moreover, negligible amounts of food were received as aid by SOC and SOL farms (2 and 4 kg/AE resp.).

The nitrogen balance in the cropping system was consistently negative. It remained stable across farm types (except LCL farms) and village level throughout the simulations (Fig. 3f). The declining nitrogen balance of LCL farms was related to the declining amount of manure they collected which represented on average 78 % of their nitrogen input. In addition, the nitrogen balance was negatively related to annual rainfall (Pearson correlation = -0.99, p-value < 0.001). In fact, given the context of low nitrogen inputs, with crop yields being mainly influenced by rainfall, the higher the yields the lower the nitrogen balance because biomass produced in fields was either harvested or grazed by livestock. Furthermore, nitrogen use efficiency at farm and village level was greater than one, reflecting soil fertility mining throughout the years (Fig. 3g). The nitrogen balance in pastureland was also negative with a decreasing trend over time (Fig. 3h). There was a strong negative correlation between rainfall and nitrogen balance in pastureland (Pearson correlation = -0.95, p-value < 0.001), because with higher rainfall, grass production and removal by livestock is increased, which is not compensated by more manure deposition.

There was no pattern in the spatial distribution of nitrogen balance and nitrogen use efficiency at village level (Fig. 4). Even though the modelled distribution of fields around farmers’ settlement was similar to observations (Figure S3), the distance between settlements and fields did not impact the spatial distribution of the nitrogen balance or the nitrogen use efficiency at village level.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Results of the model in the baseline scenario for the following outputs: Herd size (a), manure collected per farm (b), the day livestock left the village for transhumance (c), annual income (on-farm and off-farm) (d), food sufficiency (e), nitrogen balance in the cropping system (f), nitrogen use efficiency in the cropping system (g), and nitrogen balance in the pastureland (h). TLU = tropical livestock unit. LCL = land constrained livestock; MOD = market-oriented and diversified; SOC = subsistence-oriented crop; SOL = subsistence-oriented livestock. Error bars are standard deviation from 50 runs of the model.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Spatial distribution of nitrogen (N) balance (a) and nitrogen use efficiency (NUE) (b). The average of 20 years of simulations is shown. Note that NUE is not calculated for pastureland. N balance and NUE are expressed in kg N/ha and kg N/ kg N respectively.</figcaption></figure>

## 3.2. Sensitivity analysis

### 3.2.1. Farm level

The sensitivity analysis revealed variable effects across farm types and villages, with notable non-linear responses to parameter variations (Figure S4). In fact, 67 % of the coefficient of variation of the Morris elementary effects exceeded 200 % (Figure S5). On average, parameter effects were similar for SOC and SOL farms. Yields and crop residue production were mainly influenced by parameters (e.g. nitrogen uptake) directly involved in their calculation, while feed harvested per livestock unit in SOL farms was sensitive to livestock-related factors, such as feed intake during grazing and reproduction thresholds. Compared to SOC farms, SOL farms harvested less feed per livestock unit and depended more on purchased crop residues, making them more sensitive to changes in livestock parameters.

The herd size of LCL farms was particularly responsive to the proportion of grain surplus sold, as grain sales and livestock selling were key income sources. Their herd size was also affected by crop production parameters, manure excretion, and storage losses, which influenced manure availability and feed supply. Income fluctuations in LCL farms were largely driven by crop production and livestock parameters, impacting food sufficiency. The most influential factors regarding food sufficiency included grain surplus sales, manure storage losses (SOL and LCL), and nitrogen uptake coefficients (SOC and MOD). MOD farms, less sensitive overall, were most affected in their crop residue purchases. The stronger response of crop residues purchased in MOD farms was due to them having a larger herd size than SOC and SOL farms and their herd not able to leave the village. Due to their superior off-farm income, MOD farms could cope better with disturbances in their farm system, especially in the livestock sub-system by relying on external purchase of crop residues.

Off-farm income significantly influenced fertilizer purchases, herd size, manure collection, and crop residues. LCL farms exhibited the highest income sensitivity due to their dependence on livestock sales, whereas SOC and SOL farms, with the lowest incomes, showed the least sensitivity. Increasing the share of income allocated to household needs was mainly offset by additional livestock sales.

Nitrogen balance was strongly affected by nitrogen uptake coefficients and organic nitrogen inputs, particularly for sorghum productivity. LCL farms' nitrogen balance closely followed manure collection patterns, as manure was their primary nitrogen source (78 %). Nitrogen use efficiency was most sensitive to regression coefficients in nitrogen uptake equations.

Farm interactions, particularly grain exchanges, had distinct effects. Except for SOL farms, grain surplus distribution impacted income and, consequently, fertilizer application, crop residue production, and herd size. In the least food sufficient SOL farms, food sufficiency responded slightly to grain exchanges, but herd size and manure applied to crops were more affected. However, their income remained stable, as livestock sales compensated for food shortages. This highlights the role of herd dynamics in maintaining food sufficiency in resource-constrained systems.

### 3.2.2. Village level

At village level, the amount of crop residues purchased to feed livestock and the amount of feed from grazing areas within and outside the village were the most sensitive outputs. They varied most in response to changes related to yield and crop residues production parameters, grass production in grazing areas, livestock reproduction threshold and feed intake when grazing (Figure S4). The farm level sensitivity of crop yields and crop residues production to the regression coefficients included in their calculation also propagated to village level. In addition, similar sensitivity was observed at farm and village level for food given away/received by farms and the proportion of grains surplus sold. Nitrogen balance and nitrogen use efficiency were most sensitive to the same parameters at farm and village level. Livestock leaving the village for transhumance was influenced by feed intake when grazing, grass production in pastureland and the nitrogen uptake coefficient in the equations for sorghum and cowpea yield.

However, sensitivity observed at farm level did not always propagate to village level. For example, the herd size and the annual income at village level were not sensitive because of variable responses across farm types (Figure S5). Indeed, each parameter variation could lead to average positive responses in some farms and negative in others. An example is the average positive impact of varying manure excretion on the herd size of SOL farms whereas the opposite applied for MOD and LCL farms (Figure S5). Similar to the herd size, the pastureland nitrogen balance, the Gini index and the additional food purchased by households at village level remained unaffected by parameters variations. Furthermore, the results of the sensitivity analysis revealed that antagonist effects of parameters across farm types were dominant (Figure S5). Consequently, the village level magnitude of the effect of the most influential parameters was mostly inferior to the magnitude at farm level (except for MOD farms). In addition, village-level variations as a result of parameter variations differed from the weighted average of farm level variations. For example, the weighted average of proportion of change
(PC) for the amount of collected manure in response to variations in the rainfall regression coefficients for sorghum yield was 33 % versus 7 % at village level.

### 3.3. Scenario analysis

The direct exchange of crop residues for manure (and vice versa) in the baseline exchange scenario did not yield significant differences from the baseline (Fig. 5a). Although 226 farms out of the 700 farms in the village participated in the exchange, the amount of manure/crop residues exchanged each year was relatively small, especially for SOC and SOL farms (8 – 62 kg DM manure/crop residues) (Fig. 5b). Exchanges by MOD and LCL farms were much larger than by SOC and SOL farms. MOD farms mainly sent away crop residues and received manure (300 kg DM). The opposite applied for LCL farms who mainly received crop residues and sent away manure (598 kg DM). The amount of biomass exchanged was only a small fraction of what was collected/harvested. For example, SOC and SOL farms only received respectively 19 % and 3 % of the manure they collected in the baseline scenario whereas LCL

received 12 % of the crop residues harvested per TLU. This suggests that within current farm management, direct exchange of manure/crop residues was possible but the intensity of the exchange was too small to have a significant impact on the farming system.

The diversification and high-intensification scenarios had similar effects on the farming system, even if the high-intensification scenario had a slightly stronger effect especially at village level (Fig. 5a). This was due to the higher fertilizer application rate per year, caused by the lower fertilizer price, in the high-intensification, compared to the diversification intensification scenario at village level (50 kg N/ha vs. 34 kg N/ha) and at farm level especially for MOD farms (71 kg N/ha vs. 51 kg N/ha). Compared to the baseline sorghum yields increased in SOC and SOL farms and remained unchanged at village level. In both scenarios, N from legume residues incorporated in the soil was on average 30 kg N/ha while N from fertilizer applied increased from 11 kg N/ha in the baseline scenario to 25 kg N/ha for SOC and SOL farms. In LCL farms the total amount of N applied decreased from 73 kg N/ha (baseline) to 55 kg N/ha as a result of a smaller herd size. For MOD farms, the total N applied increased from 57 kg N/ha (baseline) to 71 kg N/ha in the high

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Absolute difference between alternative scenarios and baseline scenario for crop and livestock related parameters (a), direct biomass exchange (b). div and high_int are respectively the diversification and high-intensification scenarios. Cowpea yield include yields in intercropping with sorghum. CR = crop residues. DM= dry matter, TLU = tropical livestock unit. N = nitrogen, NUE = nitrogen use efficiency, CL = cropping land, PL = pastureland. LCL = land constrained livestock; MOD = market-oriented and diversified; SOC = subsistence-oriented crop; SOL = subsistence-oriented livestock.</figcaption></figure>
intensification scenario only. We simulated a reduction of sorghum yield in MOD and LCL farms, which was due not only to a decrease of N application (LCL farms only) but also to the extension of intercropping to 100 % of sorghum fields, which came with a yield penalty for sorghum (Table S6). Legumes yields increased both at farm (except LCL farms) and village level. This increase was related to the combination of the following: (1) higher nitrogen input (from legume residues) to legumes fields and, (2) in rotation, additional nitrogen from cowpea (residue) grown in intercropped sorghum fields. The nitrogen balance of the cropping system increased and became positive at village level (15 kg N/ha) and particularly for SOC (35 kg N/ha) and SOL farms (30 kg N/ha) at farm level. Nitrogen use efficiency improved and was 0.81 at village level and 0.64-0.71 at farm level. However, the nitrogen balance in pastureland worsened slightly, as a result of higher dependence of LCL farms on pastureland because of feed shortage caused by the decline in sorghum yields (Figure S6). Moreover, the livestock of LCL farms left the village 21 days earlier than the baseline scenario (Figure S6). All farm types were food sufficient and the village level food sufficiency was 125 % (vs. 113 % in baseline). However, the annual income was similar to the baseline scenario and the same applied to the inequality in income distribution (Figure S7).

Decreasing fertilizer price by 50 % (fertilizer50) resulted in increased sorghum yields at village and farm level (except for LCL farms, see below) (Fig. 5a). Corresponding to the lowest price, the fertilizer application per year at village level was the highest in this scenario (69 kg N/ha). The fertilizer50 scenario yielded a modest improvement in sorghum and cowpea yields from the baseline at village and farm level. Yields of MOD farms increased the most as they doubled their fertilizer application from 52 kg N/ha (baseline) to 124 kg N/ha. Sorghum yield increased the most at village level in the fertilizer50 scenario. Sorghum yields of LCL farms were not significantly affected by the fertilizer50 scenario as their yields in the baseline scenario were already at their maximum. However, LCL crop residue production increased allowing a bigger herd size increase and increased manure collected (Figure S6). The nitrogen balance and the nitrogen use efficiency in the cropping system were not affected and were similar to the baseline. The same applied to the nitrogen balance in pastureland despite a slight increase of the village level herd size associated with an increase in the herd size of LCL and MOD farms. However, livestock departed for transhumance one week earlier than in the baseline. Food sufficiency was reached at village level, but similar to the baseline, SOL farms did not reach food sufficiency. Food received as aid increased slightly for SOL farms who received an additional 1.6 kg of grains per year, in comparison to the baseline. The fertilizer50 was mainly advantageous for MOD and LCL annual income but did not have a significant effect on the income inequality in the village (Figure S7).

Exchanging biomass did not have a significant effect on the farming system in the diversification and high-intensification and in the fertilizer50 exchange scenarios (Fig. 5a), despite higher nutrient inputs following the application of legume residues and the decrease in fertilizer price. However, reducing fertilizer price without changing the farm management (fertilizer50 exchange) increased the number of farms exchanging biomass in comparison to the baseline exchange scenario (Fig. 5b). Nevertheless, the diversification and high-intensification exchange scenarios led to a meaningful decrease of farms exchanging crop residues/manure as well as the amount exchanged compared to the baseline exchange and the fertilizer50 exchange scenarios. In a similar way to the baseline exchange scenario, only MOD and LCL farms exchanged a considerable amount of biomass each year in the fertilizer50 exchange scenario. Nevertheless, this was still too small to influence production. Indeed, LCL farms received 47 kg of crop residues per TLU corresponding to only 4 % of the livestock feed requirement. Likewise, the difference between the amount of manure received and sent away by MOD farms in the fertilizer50 scenario was on average 333 kg which only represented 4 kg of nitrogen.

# 4. Discussion

We developed TENGA, an agent-based model to explore nutrient cycling, biomass flows and famers' interactions and livelihoods in mixed crop-livestock farming systems of West Africa. In Section 1, we discuss the sensitivity of the model to its parameters at farm and village level. Additionally, we evaluate the model outputs regarding findings from literature and explain the processes behind the model's results in Section 4.2. In Section 4.3 we discuss opportunities to improve the productivity of mixed crop-livestock farms at farm and village level. Finally, Section 4.4 presents the limits of the study as well as future improvement and use of the model.

## 4.1. Sensitivity of farm and village indicators to model input parameters

ABM exploration remains in its infancy for West African farming systems although a limited body of modelling approaches and applications is gradually taking shape and is beginning to provide insights into model behavior and farming systems functioning (Chapron et al., 2026; Delay et al., 2025b). Our sensitivity analysis revealed that village level indicators were sensitive to crop, livestock and household-related parameters. However, these parameters affected the village and farm level differently and village level results differed from a simple extrapolation or weighted average of farm level results (Grillot et al., 2018). This pointed out the importance and added value of using an ABM as it includes interacting individual farms allowing multiple-level analysis of regressive, additive or synergy effects in a farming system (Bert et al., 2011; Marvuglia et al., 2022). In our study, for most parameters a regressive effect, i.e. a reduction in the proportion of change from the baseline, was noticed when moving from farm to village level, due to the non-linear distribution of the impacts of tested parameters at farm level. Furthermore, the sensitivity analysis shed light on the mechanisms governing the propagation of parameter variation effects from one level of the farming system to other levels. From plot to farm and village level, the determinants of crop and grass production (rainfall and nitrogen uptake regression coefficients) had a consistent influence on every level of the farming system. This was explained by primary production in the system having a direct influence on livestock production, income and possibilities of interactions between farms. Hence, improving plot level nutrient and water use efficiency remains critical in the system and can be achieved by coupling technical innovations with recommended plot management. Technical innovations are constantly investigated through the breeding of varieties making the most out of the limited resources (water and nutrients) available (Zampaligré et al., 2022), and the development of plot level options to improve yields and soil fertility (Garba et al., 2018; Raboin et al., 2023). However, making relevant recommendations remains challenging in a context of heterogeneous soil fertility with limited organic/mineral inputs and livelihoods (Vanlauwe et al., 2015). Beyond the plot level, the need for tailored interventions increases. In fact, meaningful livestock and household-related parameter effects at farm level did not always propagate to village level due to antagonist impacts across farm types. This implies that the success of interventions is intimately linked to their suitability to the diversity of farms (Périnelle et al., 2022; Ronner et al., 2018). Additionally, while plot and farm level interventions can affect village level performance, the reverse is also true. Indeed, interactions between farms reflected back on their crop and livestock production, highlighting the opportunity to influence the system through social interactions.

The sensitivity analysis revealed that several parameters affected each farm type differently. Subsistence farms were more affected by rainfall and nitrogen parameters which are considered the main limiting factors for crop production in the area (Diarisso et al., 2016; Sanou et al., 2023). Hence, subsistence farms would benefit the most from interventions aiming at improving crop yields. However, in the current context of biomass scarcity, the higher dependence of subsistence farms
on external feed exacerbated the vulnerability of their livestock system. The same applied for agro-pastoralist systems whose reliance on grazing areas made them vulnerable, because the competition for land represented a serious threat to their livestock management. Competition for common resources such as grazing areas and water points is expected to increase in SSA because of agricultural land expansion and fragmentation driven by demographic growth (Giller et al., 2021a). There is therefore a need to rethink the pastoral management via the reorganization of transhumance routes at a regional level. Furthermore, the integration of pastoralists into locally recognized communities managing common grazing areas, as observed in Senegal, may also help to ease social tensions and strengthen collective governance of shared pastoral resources (Delay et al., 2025a).

Beyond agronomy, this study calls attention to social organization as a key lever to transform farming systems. We showed in the sensitivity analysis that social organization can act like a safety net for the most vulnerable farms, highlighting its importance for the least food sufficient farms of the village, results that are consistent with those of Chapron et al. (2026) in Senegal. Indeed, farmers' organizations (e.g. cooperatives) offer the potential to improve production and access to market by building on synergies of farmers' resources (Sims et al., 2016). Additionally, we demonstrated that the diversification of income through off-farm activities, remains pivotal to better cope with disturbances in the farm systems. Our results are in line with the findings of Babatunde and Qaim (2010), Fraval et al. (2020) and Giller et al. (2021b) who demonstrated the importance of off-farm revenue on food security and income in SSA.

## 4.2. Model performance and simulated patterns

In the baseline scenario, simulations produced relatively stable yields, with a trend clearly related to rainfall. This was expected as rainfall was part of the regression equations used to calculate yields and because of the low level of fertilizer application. The correlation between rainfall and crop yields in Burkina Faso was recently shown also by an empirical study by Sanou et al. (2023). Our simulations revealed negative N balances in the cropping system for all farm types and at village level. This is in line with the results of Diarisso et al. (2015) and Grillot et al. (2018) who also found negative N balances in the cropping system at farm and village level in mixed farming systems of semi-arid West Africa using an empirical and agent-based modelling approach respectively. NUE was always greater than one, implying N mining because of larger N export than N inputs. The best NUE was simulated for agro-pastoralists (LCL farm type) who could afford to apply large amounts of manure to relatively small fields. Our results suggest that the contribution of free grazing on crop residues left on fields to livestock feeding in our study area is negligible. Conversely, Andrieu et al. (2015) found in the Sudanian zone of Burkina Faso, that non-access to crop residues on fields by big livestock owner's herd would have a significant negative impact on their livestock production. The difference in results lies in the fact that, in our study area, the fraction of crop residues left on fields was smaller (0–30 % vs 80 %) and agro-pastoralists had the possibility to leave the territory in search for green pasture. Hence, the reliance on forage from pastureland inside and outside the village was key for big livestock owners in our study area (Assogba et al., 2023). Additionally, our results indicated that livestock leaving the village in search for pasture in the southern and more humid regions of the country occurred at the onset of the rainy season and remained consistent irrespective of the annual rainfall amount. This is confirmed by empirical studies on livestock mobility in Burkina Faso (Dongmo et al., 2012; Ilboudo et al., 2025). We found that pastureland had a negative N balance caused by the fact that N inputs from livestock dung could not compensate the N removal due to grazing. A similar result was found in ABM studies by Grillot et al. (2018) in pastureland of semi-arid Senegal in West Africa. Given the importance of pastureland in supporting livestock production in semi-arid West Africa, proper management to

avoid degradation (Le Houérou, 2000) seems key to sustain livestock production.

In TENGA, the simulated herd size increased, from the initial values, especially for subsistence farms that represented 68 % of farms in the village. However, all herd sizes stabilized five to ten years after the start of the simulation when an equilibrium was reached between the feed resources and livestock requirements. This initial increase can be explained by the mechanisms and input parameters implemented in the model, especially for livestock feeding, reproduction and mobility. Our results also suggested that it was possible for all farm types to increase their herd size. However, the mobility of agro-pastoralist farms would be affected. Indeed, with current farm management, a larger herd size in the village would translate into higher pressure on grazing areas, on which agro-pastoralist farms depended the most. This would force them to leave the village earlier in response to increased fodder scarcity.

At village level, food sufficiency was always reached even though it required additional purchase of grains. However, at farm level, not all farms were food sufficient despite food received as help from households with food surpluses. Indeed, on average four years out of the 20 years of simulation, 10 % of farms were not food sufficient and did not have enough income to purchase additional grains. Fraval et al. (2020) also found similar results concerning food sufficiency in semi-arid Burkina Faso.

We found no spatial pattern in N balance and NUE in the village. This could be explained by (1) the spatial configuration of the settlements in the village which was a mix of small and large hamlets, (2) grazing areas were scattered over the village and (3) in the model we did not assume that manure distribution would be related to the distance between fields and farmers' settlements as this was not observed in the village (Assogba et al., 2023; Diarisso et al., 2016).

## 4.3. Opportunities to improve crop-livestock integration and production at farm and village level

We investigated opportunities for agricultural intensification through higher nutrient inputs and direct exchange of crop residues and manure between farms. Two options of agricultural intensification were investigated: policy interventions via subsidies and local adjustment of cropping systems via crop diversification.

We showed that whichever intensification option, increased nutrient inputs were beneficial to crop and livestock production at village level. Similar results were found at village level by Rufino et al. (2011) in Zimbabwe. However, the source of additional nutrients led to drastically different results. The increase of nitrogen input through crop diversification with legumes and the use of legumes residues as organic input was mainly beneficial to subsistence farms. At village level, increased crop diversification resulted in legume yield benefits and improved nitrogen use efficiency, without compromising sorghum yield and the herd size. Therefore, in low nutrient input systems with depleted soils, diversification with legumes offers an opportunity to shift from a vicious cycle of nutrient mining to improved soil fertility management. Additionally, the absence of a yield penalty for the staple crop makes it an attractive option for farmers. However, we also demonstrated that diversification with legumes is not a one-size-fits-all-solution, as farm level results partly contrasted with the village level. At farm level, the increase of the herd size of subsistence and market-oriented farms combined with the lower feed availability for agro-pastoralists farms led to heightened competition for fodder in pastureland. Hence, there was a substantial decrease of agro-pastoralist herd size. Furthermore, excess of nitrogen was noted indicating that the amount of legume residues or fertilizer applied could have been reduced. Therefore, part of the legume residues could be used to feed the livestock. In fact, in mixed crop-livestock system there is often the dilemma of using crop residues to feed the soil or the livestock (Rodriguez et al., 2017). Our results indicated that in a context of biomass scarcity, increased diversification associated with the recycling of legume residues as organic amendment
is a promising way to sustain crop and livestock production as well as food sufficiency in farms with limited herd size. However, farms with larger livestock herds did not benefit from expanding the traditional intra-hill intercropping to all sorghum fields. The competition with cowpea led to a sorghum yield penalty in the traditional intra-hill intercropping (Ganeme, 2022) and a feed deficit that a 25 % decrease in the fertilizer price could not compensate.

Agricultural intensification relying solely on subsidies to reduce the fertilizer price affected farm types differently but benefited village-level yields. A 50 % reduction of the fertilizer price allowed wealthy market-oriented farms to almost reach maximum yield in the model and increase their herd size and income. Similarly, agro-pastoralist increased their crop residue production and their herd size in this scenario. However, only modest impacts occurred on crop and livestock production of subsistence farms. Our results support the fact that although fertilizer subsidies can increase food production, their benefits are often not evenly spread across farmers in SSA (Jayne et al., 2018). In comparison to scenarios with crop diversification, even a drastic reduction of the fertilizer price did not benefit subsistence farms which remained non-self-sufficient. Therefore, targeted interventions in farming systems continue to be essential (Berre et al., 2022; Périnelle et al., 2022). Additionally, only reducing the fertilizer price resulted in similar nitrogen balance and nitrogen use efficiency as the baseline scenario, which makes sense as the farm management did not change. The additional N that is added, was also removed because of the increased yields. Interventions solely based on subsidies or excluding improved nutrient management are not likely to sustain food sufficiency and result in improved crop-livestock integration, especially in semi-arid West Africa.

Furthermore, our results demonstrated that fostering village level nutrient recycling through direct exchange of crop residues and manure was best achievable with the current farm management which prioritizes the use of crop residues as feed. This exchange was meaningful only between market-oriented farms cultivating the largest area and agro-pastoralists farms owning the largest herd size and cultivating the smallest area. Therefore, any scenario resulting in lower feed production and herd size in market-oriented and agro-pastoralists farms respectively, will reduce the amount of biomass exchanged. Nevertheless, the complementarity between these farms in terms of feed (market-oriented) and manure (agro-pastoralist) is currently not strong enough for biomass exchange to have a significant impact. An alternative could be to foster exchange between crop and livestock-oriented regions within the country (Ryschawy et al., 2022). In this case the level of crop and livestock production should be significantly higher than the current one to allow the amount exchanged to have a real impact on farming systems.

Interestingly, none of the scenarios tested could substantially reduce inequity in farm income in the village even if on-farm income increased for most farms. The most important income gap was between market-oriented farms and other farms. The main reason was the higher off-farm income of market-oriented farms, and the fact that off-farm income was kept within the observed range of each farm type in the simulations. This suggests that beyond farm management options that increase on-farm income, social interventions to create jobs outside agriculture appear essential (Falconnier et al., 2018).

## 4.4. Further improvement and use of the model

In the current version of TENGA, farmers' decision-making has little flexibility. Their decision-making was based on mechanistic rules rather than behavior theories (Speelman, 2014). More flexible decision-making can help to better understand the impact of social interactions and organizations on the farming system. A possible development of the model could also include demographic growth and its effects on land use. This would require >20 years of simulations. In addition, labor requirements for cropping activities and livestock keeping are not included in the current version of the model. Likewise, labor availability is currently

absent, even though it is a major factor affecting whether a certain innovation can be adopted or not (Arslan et al., 2022). Further use of the model can explore how different farm management options (e.g. fertilizer and land allocation to crops) affect the farming systems at farm and village level (Chérel et al., 2015). This could allow to explore the diversity of model outputs regarding various farm management and associated trade-offs within and across levels in the farming system.

## 5. Conclusion

We developed TENGA to simulate and explore biomass production and flows, nitrogen balance and nitrogen use efficiency as well as farmers' interactions and livelihoods at farm and village level. Our study revealed the importance of a bottom-up approach, such as ABM, to study farming systems at various levels. Indeed, we showed that village level results were not an aggregation of results at farm level but also depended on interactions within and outside the village. The model was useful in identifying non-linearity, interactions and feedback among the system components and across levels, thereby allowing to better grasp the complexity of the system. We highlighted key parameters related to crop yields, livestock feed intake and reproduction, farm management and social organization that offer pathways to improve crop-livestock integration.

Furthermore, our study demonstrated that pathways to improve crop-livestock production and integration depend on the diversity of farms. We showed that farm level interventions can influence interactions at village level with feedback at farm level, supporting the relevance of multi-level studies of farming systems. In addition, we showed that higher nutrient inputs can benefit crop and livestock production at village level but not necessarily at farm level. Furthermore, pathways to higher nutrient inputs also matter which calls for targeted interventions that take into account farm heterogeneity. When other conditions remain unchanged, we showed that opportunities for improving nutrient recycling lie more at farm than at village level. This implies that, if one aims to change the situation at the village level, such change cannot occur without close support to local inhabitants. We discussed that a focus on farm related options only would not likely reduce income inequity at village level. Social interventions such as job creation outside agriculture are important to increase livelihoods and potentially reduce income inequity.

## Declaration of generative AI in scientific writing

During the preparation of this work the authors used ChatGPT for proofreading purposes and language editing. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## Data statement

All data and source code used to build and run the TENGA model are publicly available on Zenodo: https://doi.org/10.5281/zenodo.17976026

## CRediT authorship contribution statement

**Gildas G.C. Assogba:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. **David Berre:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Conceptualization. **Myriam Adam:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. **Etienne Delay:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Formal analysis, Conceptualization. **Erika Speelman:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology,
Conceptualization. Katrien Descheemaeker: Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We thank the McKnight foundation which funded this research through the 3F (Feed the soil and Feed the livestock to Feed the people) project. We also thank the CUOMO foundation for providing additional scholarship. Finally we thank farmers, agronomists and researchers from Burkina Faso who helped us collect the data that form the basis of this work.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ecolmodel.2026.111478.

## Data availability

Data will be made available on request.

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