# The influence of meteorological variability on herbaceous biomass production in a Sahelian semiarid savanna ecosystem (Dahra, Ferlo, Senegal)

Ousmane Diatta, Torbern Tagesson, Ousmane Ndiaye, Mohamed Badji, Simon Taugourdeau

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Ousmane Diatta, Torbern Tagesson, Ousmane Ndiaye, Mohamed Badji, Simon Taugourdeau. The influence of meteorological variability on herbaceous biomass production in a Sahelian semiarid savanna ecosystem (Dahra, Ferlo, Senegal). Grasses, 2025, 4 (4), pp.52. 10.3390/grasses4040052. hal-05424973

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Article

# The Influence of Meteorological Variability on Herbaceous Biomass Production in a Sahelian Semiarid Savanna Ecosystem (Dahra, Ferlo, Senegal)

Ousmane Diatta<fnref n="1" /> ID, Torbern Tagesson<fnref n="2" /> ID, Ousmane Ndiaye<fnref n="1" /> ID, Mohamed Badji<fnref n="3" /> and Simon Taugourdeau<fnref n="4" />,∗ ID

<figure><img src="image_2.png" /></figure>

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Academic Editor: Fabio Gresta

Received: 30 August 2025

Revised: 19 November 2025

Accepted: 3 December 2025

Published: 10 December 2025

**Citation:** Diatta, O.; Tagesson, T.; Ndiaye, O.; Badji, M.; Taugourdeau, S. The Influence of Meteorological Variability on Herbaceous Biomass Production in a Sahelian Semiarid Savanna Ecosystem (Dahra, Ferlo, Senegal). *Grasses* **2025**, *4*, 52. https://doi.org/10.3390/grasses4040052

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Abstract

Savanna ecosystems of the Sahel are heavily affected by climate change, leading to drier subtropical regions. These ecosystems play a fundamental role in food security of the region, so that an improved understanding of how these ecosystems are affected by these weather events is thereby critical. Several studies have assessed the herbaceous production level, dynamics during the rainy season, and biotic and abiotic factors that could impact this production. Some authors argue that rain is the main factor positively influencing the biomass production in semi-arid areas where the rainfall ranges between 200 and 750 mm. This study aims to assess the contribution of some meteorological variables to biomass production in a Sahelian semiarid savannah. From 2008 to 2018, rainfall (mm), soil moisture (%), soil temperature (°C) at 5 cm depth, number of times there was no rain (NTNR), number of rainy days, and the herbaceous biomass were monitored. The contributions of meteorological variables to biomass production were calculated using the XGBoost regression model. The most influential meteorological parameters on herbaceous biomass production are soil temperature, amount, and distribution of rainfall.

**Keywords:** Sahel pastures; meteorological variability; herbaceous biomass

## 1. Introduction

Savanna ecosystems of the Sahel are already heavily affected by climate change [1–3]. Many subtropical regions are expected to become drier due to climate change, leading to reduced vegetation, which may in turn amplify the initial drying [2]. These ecosystems play a fundamental role in the region’s food security, and an improved understanding of how these ecosystems are affected by these weather events is thereby critical. The herbaceous layer is essential for the livestock, and approximately 300 million animals in the Sahel rely on it as their main food source [4]. This herbaceous layer is mainly used to feed the livestock during the rainy season, while the woody layer is mostly used during the dry season [5].

Several studies have assessed the herbaceous production level, dynamics during the rainy season, and biotic and abiotic factors that could impact this production. These studies have shown that the biomass dynamic follows the amount of rainfall, with a yearly average

<footnote n="1">¹ Zootechnical Research Centre, Senegalese Agricultural Research Institute, Dahra BP 01, Senegal; sadiousmane@hotmail.com (O.D.); fisco42000@yahoo.fr (O.N.)</footnote>

<footnote n="2">² Department of Physical Geography and Ecosystem Science, Lund University, SE-22362 Lund, Sweden; torbern.tagesson@nateko.lu.se</footnote>

<footnote n="3">³ Mathematics Department, UFR of Sciences and Technologies, Gaston Berger University, Saint-Louis BP 234, Senegal; mohamed.badji@ugb.edu.sn</footnote>

<footnote n="4">⁴ UMR SELMET CIRAD, Rabat 6202, Morocco</footnote>

<footnote>* Correspondence: simon.taugourdeau@cirad.fr</footnote>
biomass yield of ~2.6 kg dry matter (DM) ha⁻¹ [4,6,7]. The rainy season in the Sahel is brief, typically lasting from July to October. The herbaceous vegetation germinates a few days after the rainy season starts, then increases its production until reaching its maximum in October [4].

However, many factors other than rain can play a role in the vegetation dynamics, such as soil type, topography, pasture exploitation [8], combining effect of forage removal (by grazing and herbivorous insects) and trampling during the dry season [9]. Since 60% of the root biomass is located in the 0–10 cm layer, and only 10% below 40 cm [8], conditions in the upper soil layer are another important factor, heavily affecting the dynamics in the herbaceous plant production.

This study aims to determine which meteorological parameters govern biomass production in a Sahelian semiarid savannah ecosystem and assess the contribution of each climate variable. Knowing which meteorological variables contribute to standing herbaceous biomass would allow a better understanding of the dynamics of Sahelian ecosystem functioning. It will also help in predicting the dynamics of Sahelian ecosystems' production in relation to the current climate trend.

## 2. Material and Methods

### 2.1. *Study Area*

This study was conducted in Dahra, in the Senegalese Sahel, located in the northern half of the country, between 15°20' North and 15°29' West (Figure 1). The rainy season is ~3 months long from the beginning of July to the end of October. The Sahelian climate that governs this area induces approximately 372 mm rainfall, 1.45 m·s⁻¹ wind speed, 8 h per day insolation, and 30 °C average temperature [7]. Since 2018, the area has been widely dominated by *Diodella sarmentosa*, an invasive species of the Rubiaceae family [4]. Due to this situation, the contribution of the other species to biomass production is currently very low.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Geographic and administrative location of Dahra.</figcaption></figure>

### 2.2. *Biomass Sampling Design*

The biomass sampling design is a square with sides of 750 m, with a flux tower at its center, divided into nine smaller squares with sides of 250 m. Twenty-eight sampling points were placed along two 1 km-long perpendicular transects so that, outside the central small square, the sampling points were 120 m apart. Inside the central small square, the sampling points were 50 m apart. Biomass data were collected from these 28 points
(14 points per transect) (Figure 2). To avoid resampling the same points sampled on the previous measurement day, the entire sampling grid was shifted one meter northward each measurement day during the rainy season.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Data sampling design. The difference in the distance between sampling points (black squares) inside and outside the central square arises from the need for more sampling points near the flow tower for calibration.</figcaption></figure>

## 2.3. *Biomass Data Collection*

The biomass was assessed during the rainy seasons (July to October) from 2008 to 2018, using the integral harvesting direct method described by [10]. This started ten days after the first rain, which was sufficient to allow germination in the herbaceous layer. Then, the biomass was collected every 10 days throughout the rainy season [4]. After collection, the biomass samples were dried in an oven at 80 °C until they had a constant weight. Each year, the biomass of the 28 plots is averaged for each measurement date, and the maximum average is used as the annual biomass amount.

## 2.4. *Meteorological Data*

As biomass data, the hydrological and meteorological data were collected from 2008 to 2018. The measured variables were soil moisture (%) and soil temperature (°C) at 5 cm depth, and rainfall. All sensors were connected to a CR-1000 logger in combination with a multiplexer (Campbell Scientific Inc., North Logan, UT, USA). Data were sampled every 30 s and stored as 15-min averages (sum for rainfall) [11]. Then we estimated the number of times there was no rain (NTNR) between the first and last rain events. The number of rainy days is the number of days it rained during the rainy season. These variables are important for biomass production [12].

## 2.5. *Data Processing*

The contributions of meteorological variables to biomass production were calculated using the Extreme Gradient Boosting (XGBoost) regression model. It is a supervised machine learning method that is particularly effective for prediction tasks. Since the data were collected in ten (10) consecutive years, we tested the autocorrelation of the target variable (biomass) using the Ljung-Box test. The latter didn't detect significant autocorrelation in the temporal series of biomass (p-value = 0.12), indicating a relative independence in observations between successive years. To apply the XGBoost model, we first separated the target variable (biomass) from the explanatory variables. Then, the dataset was converted to a matrix format for training with XGBoost. The model was trained
to minimize the squared error between the predicted and measured values. Finally, an
importance matrix was generated to assess the relative influence of each variable. Ten-fold
cross-validation has been applied to validate the model.

This method quantifies the contribution of meteorological variables to biomass produc-
tion but does not reveal the nature of their relationships (i.e., positive or negative). There-
fore, to understand these relationships between biomass and the explanatory variables—
soil moisture, soil temperature, rainfall, number of rainy days, and number of times there
was no rain—we performed a Principal Component Analysis (PCA) with biomass as a
supplementary variable before applying the XGBoost model.

**3. Results**

3.1. *Relationships Between Biomass and Meteorological Variables*

The first principal component explained 62.2% of the interannual variability in the biomass (Figure 3). Together with the second component (18.71% of the variance), they explain 80.92% of the variability. These two components are considered for the rest of the study to help understand the relationships between the variables.

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Relationships between variables. The blue color indicates that the biomass variable was not considered when realizing the PCA.</figcaption></figure>

The rainfall variables (rain, number of rainy days, and NTNR) seem positively corre-
lated with the biomass, while the temperature variable appears negatively correlated with
the biomass and rainfall variables.

Soil moisture doesn't show any correlation with the biomass and temperature variables.

3.2. *Meteorological Variables' Contribution to the Standing Biomass Amount*

The ranking of meteorological variables according to their contribution to herbaceous
biomass production is shown in Figure 4. The soil surface layer (5 cm) temperature is
the most important variable, followed by the amount of rain and the NTNR. The soil
moisture (5 cm depth) and the number of rainy days have a very low impact on herbaceous
biomass production.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Meteorological variables' contribution to the herbaceous biomass.</figcaption></figure>

### 3.3. XGBoost Model Validation

According to the cross-validation, the model generalizes well, as evidenced by the close alignment of the test and training error curves (Figure 5). The Root Mean Square Error (RMSE) fell sharply during the initial 20 iterations, reached stability by iteration 40, and converged to a final value of 410.8. The grey band surrounding the curves represents the dispersion of the RMSE across the folds and is not a 95% confidence interval.

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Cross-validation of the XGBoost model. During 10-fold cross-validation, the RMSE was computed for each fold on both the training and test sets. The mean and standard deviation of the RMSE across all folds were then determined. The grey band in the figure represents the mean ± standard deviation, capturing the dispersion of the RMSE across the folds. It is important to note that this interval reflects the variability of the RMSE between folds and is not a 95% confidence interval. This visualization effectively highlights the model's stability and the consistency of its prediction error.</figcaption></figure>
4. Discussion

Soil temperature exerts dual and opposing effects on ecosystem processes. Positively, it enhances litter decomposition, thereby releasing essential nutrients into the soil and increasing their availability for plant uptake. Conversely, elevated soil temperatures accelerate the drying of the surface soil layer, which reduces water availability and imposes hydric stress on plants. On the one hand, ref. [8] argues that during the first hours following the wetting, the evaporation rate is very high, due to the high temperature at the soil surface. On the other hand, ref. [13] highlights that the stocks of organic matter in soils result from the balance between inputs and outputs of carbon within the belowground environment. Inputs are primarily from leaf and root detritus. Outputs are dominated by the efflux of carbon dioxide (CO2) from the soil surface. The production of CO2 in soils is almost entirely from root respiration and microbial decomposition of organic matter. Like all chemical and biochemical reactions, these processes are temperature-dependent. In this study, we hypothesize that increased soil temperature decreased herbaceous biomass production due to the soil's low organic matter content in the Ferlo area [14].

A positive correlation was observed between biomass, the number of rainy days, and the number of times there was no rain (NTNR). Rain is known to have a positive effect on the amount of biomass in the Sahel [6,12,15]. The germination of the herbaceous layer depends on the onset of the rainy season, which usually occurs in late June or early July. The germination is followed by the root system installation and the plant tillering. After that, the amount of biomass increases as the rainfall increases from July to October. The biomass peak is reached in October [4], just after August and September, the rainiest months [16] in the Sahel region. This makes the positive correlation of biomass with the number of rainy days and the number of times there was no rain understandable. The latter is like an indicator of the rain distribution over the rainy season [12]. The rain distribution, which differs from the rain frequency, describes the spacing of rainfall throughout the rainy season. A favorable distribution features non-consecutive rain events separated by dry intervals of several days. Those intervals refer to what is named NTNR in this study. The higher the NTNR, the better the rain distribution.

No correlation was observed between biomass and soil moisture. However, studies have shown that soil moisture has a positive effect on biomass production [17]. When solar radiation reaches the herbaceous layer, a portion is absorbed by the biomass, and the remainder is reflected—a process that similarly occurs when sunlight reaches the soil surface. Following rainfall, heat from solar radiation drives evapotranspiration, which combines plant transpiration and soil evaporation. This leads to a reduction in soil moisture and subsequent drying of the soil. Beyond the influence of topography on runoff, soil water content is also strongly influenced by temperature. During the flowering stage of herbaceous plants, the distribution of the root system weight shows that approximately 60% of the total root mass is concentrated in the 0–10 cm soil layer [8]. This indicates that water availability in the surface soil layers is more critical for herbaceous vegetation than the amount of rainfall. These findings highlight the ecological significance of surface soil moisture for sustaining and enhancing the productivity of the herbaceous layer in this Sahelian region. The difference with our results could be due to the depth at which the soil moisture has been measured. [17] considered the moisture in the top 10 cm layer of soil, while we used the top 5 cm layer. Besides, the Dahra site was formerly dominated by the grasses [7,18], which are C4 species. But since 2018, pastures have been dominated by the Rubiaceae family species *Diodella sarmentosa*, a C3 species that seriously affects the herbaceous species composition and specific diversity [4]. This difference in the type of photosynthesis could be decisive. C4 photosynthesis is advantageous under low atmospheric CO2 and/or high temperatures. The advantages of C4 photosynthesis occur in
lower CO₂ environments and/or high temperature environments, where photorespiration rates are relatively high in C3 plants [19]. Photorespiration induces a loss of energy and carbon, resulting in a decrease in photosynthesis efficiency.

The described soil temperature effect on the soil moisture includes that for two years receiving the same amount of rain, the standing biomass amount would be higher in the year during which the soil temperature is lower. According to [8], soil temperature is a key factor affecting the rate of chemical and biological processes in the soil, essential to plant growth. The rain effect in biomass production is secondary to the soil temperature effect. This is proven by the stronger correlation between biomass and soil temperature than between biomass and rain. Thus, logically, regarding the weight of meteorological parameters in biomass production, the soil temperature is more influential, followed by the rain and the NTNR.

**5. Conclusions**

The most influential meteorological parameters on herbaceous biomass production are soil temperature, amount, and distribution of rainfall, in that order. Temperature has a negative effect on biomass, while rain and the rainfall distribution have a positive effect.

Still, more investigations should be done to better explain the relationship between herbaceous biomass and soil moisture, in relation to the photosynthesis type (C3, C4) of the herbaceous dominant species.

The study results are based on the Senegal Ferlo region, and their universality needs to be verified by other subregions of the Sahel. Biomass response dominated by an invasive species may have a particularity in the results. We expect that biomass dynamics in plots dominated by an invasive species will show a characteristic signature, differing from those in native-dominated communities.

**Author Contributions:** Conceptualization, O.D. and S.T.; methodology, O.D., S.T. and M.B.; software, O.D. and M.B.; validation, O.D., S.T., O.N., M.B. and T.T.; formal analysis, O.D.; investigation, O.D., S.T. and M.B.; resources, O.D., S.T. and T.T.; data curation, O.D.; writing—original draft preparation, O.D.; writing—review and editing, O.D., S.T., M.B., O.N. and T.T.; visualization, O.D., O.N., S.T. and T.T.; supervision, S.T., T.T. and O.N.; project administration, T.T.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by [FORMAS] grant number [Dnr 2021-00644], [Carl Tryggers Stiftelse, and Fysiografen], [Swedish National Space Agency] grant number [SNSA Dnr 2021-00144; 2021-00111], and the APC was funded by [CIRAD].

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author to: sadiousmane@hotmail.com.

**Conflicts of Interest:** The authors declare no conflict of interest.

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