# A forecasting model for desert locust presence during recession period, using real-time satellite imagery

Lucile Marescot, Elodie Fernandez, Hichem Dridi, Ahmed Salem Benahi, Mohamed Lemine Hamouny, Koutaro Ould Maeno, Maria-José Escorihuela, Giovanni Paolini, Cyril Piou

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## To cite this version:

Lucile Marescot, Elodie Fernandez, Hichem Dridi, Ahmed Salem Benahi, Mohamed Lemine Hamouny, et al.. A forecasting model for desert locust presence during recession period, using real-time satellite imagery. Remote Sensing Applications: Society and Environment, 2025, 37, pp.101497. 10.1016/j.rsase.2025.101497. hal-04995261

HAL Id: hal-04995261

https://hal.inrae.fr/hal-04995261v1

Submitted on 18 Mar 2025

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# A forecasting model for desert locust presence during recession period, using real-time satellite imagery

Lucile Marescot<sup>a, *</sup>, Elodie Fernandez<sup>a</sup>, Hichem Dridi<sup>b</sup>, Ahmed Salem Benahi<sup>c</sup>, Mohamed Lemine Hamouny<sup>b</sup>, Koutaro Ould Maeno<sup>d</sup>, Maria-José Escorihuela<sup>e</sup>, Giovanni Paolini<sup>e</sup>, Cyril Piou<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Automatic forecast system

Locust outbreak

Machine learning

Remote sensing

Schistocerca gregaria

## ABSTRACT

Desert locust (Schistocerca gregaria) is a major agricultural pest that poses significant socio-economic challenges to food security. This study aims to enhance preventive management of desert locusts in Western and Northern Africa by improving an operational model developed by Piou et al. (2019). The model employs satellite remote sensing data and machine learning to forecast locust occurrence at a 1 km² resolution every ten days. Objectives include identifying environmental risk factors, training random forest models with high-predictive power and providing updated forecasts via a web interface. It is the first implementation of a statistical forecasting model for this species within an automated system, delivering updated locust presence probabilities every ten days. Validated through field surveys with a positive error rate of 23%, the forecasting tool shows a strong correlation between predicted probabilities and observed locust densities. This operational tool can guide survey teams, optimize resource allocation, and mitigate environmental impacts efficiently. We believe continuous evaluation and integration of the forecast system will enhance its effectiveness in preventing locust outbreaks, thereby safeguarding food security in the region.

## 1. Introduction

Environmental disasters and biological hazards, including wildfires, floods, epidemics, and pest outbreaks, present significant socio-economic challenges that can be intensified by climate change and natural habitat destruction (Alcántara-Ayala, 2002; Sivan-pillai, 2023). Among these, pest outbreaks such as those caused by the desert locust (Schistocerca gregaria) stand out due to their devastating effects on agriculture and food security. Desert locusts exhibit phenotypic plasticity, transitioning from solitary to gregarious phases in response to environmental signals and density-dependent mechanisms, leading to massive swarms capable of

<footnote><sup>a</sup> CBGP, INRAE, IRD, CIRAD, Institut Agro Montpellier, Montpellier University, Montpellier, France</footnote>

<footnote><sup>b</sup> Food and Agriculture Organization of United Nations, Alger, Commission de Lutte Contre le Criquet Pèlerin en Région Occidentale (CLCPRO), France</footnote>

<footnote><sup>c</sup> Centre National de Lutte Antiaclidienne (CNLA), Mauritania</footnote>

<footnote><sup>d</sup> Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Japan</footnote>

<footnote><sup>e</sup> isardSAT, Parc Tecnologic Barcelona Activa, Barcelona, Spain</footnote>

<footnote>* Corresponding author. Centre de Biologie pour la Gestion des Populations, 755 Avenue du Campus Agropolis CS 30016 34988 Montferrier sur Lez, France.</footnote>

<footnote>E-mail address: Lucile.marescot@cirad.fr (L. Marescot).</footnote>
traveling up to 150 km/day (Pener and Simpson, 2009; Steedman, 1990). Early and accurate forecasting of locust occurrence is crucial to reducing the scale of damage and mitigating risks to ecosystems and livelihoods (Sword et al., 2010).

Preventive management is a long-term planning approach designed to anticipate, forecast, mitigate, and prepare for potential crises before they escalate. This strategy involves identifying early warning signals, understanding environmental and ecological risk factors, and implementing measures that reduce the likelihood of severe outcomes, through regular surveillance and actions (Scheffer et al., 2009; Hewitt, 2013). For desert locusts, preventive management focuses on monitoring environmental conditions conducive to species aggregation, enabling targeted interventions such as breeding site disruption or localized pest control (Sun et al., 2022; Ellenburg et al., 2021). This is distinct from reactive management, which primarily addresses crises after they occur, often focusing on large-scale responses to locust swarms through widespread pesticide application or emergency relief efforts (Showler, 2019; Giardino et al., 2012; Cressman, 2008). While reactive management is essential for addressing immediate impacts, it tends to be costlier, more environmentally damaging, and less effective at curbing long-term risks. In contrast, preventive management emphasizes sustainability, resource efficiency, and minimizing environmental harm by intervening early in the locust lifecycle and disrupt opportunities to breed or aggregate (Piou and Marescot, 2023).

In recent years, the integration of satellite remote sensing and machine learning techniques has proven transformative in environmental risk management and species distribution modeling (Manfré et al., 2012). Numerous studies have employed these tools to address locust invasions (Piou et al., 2019). For example, Guan et al. (2021) trained MaxEnt models on presence-only data to predict future habitat of solitary desert locusts under climate change scenarios, while Kimathi et al., 2020 used similar models with bioclimatic variables such as soil moisture and rainfall to identify breeding sites. Although such models are valuable, they are limited for real-time forecasting approach. Many focus on locust swarms, often addressing reactive management rather than preventive strategies (Shao et al., 2021; Khan et al., 2024; Mitra et al., 2024). Others are designed for long-term projections (Guan et al., 2021) or employ static environmental variables without capturing the dynamic factors that influence locust ecology over time (Cornejo-Bueno et al., 2021; Saha et al., 2021).

The present study seeks to bridge these gaps by developing a comprehensive and operational forecasting model for preventive locust management over an extensive area encompassing ten countries in Western and Northern Africa. Unlike models using static predictors (Cornejo-Bueno et al., 2021), our approach integrates dynamic satellite-derived variables such as normalized difference vegetation index (NDVI), land surface temperature, and precipitation, measured at different intervals. This allows for establishing statistical relationships between observations and present and past environmental conditions, as seen in Gómez et al. (2018), Sun et al., 2022 and Piou et al. (2013, 2019). Our study emphasizes on extended forecasts at the regional scale, integrating eco-regional data to account for shared biotopes and soils across borders rather than focusing on isolated regions. It is based on applying random forest to presence/absence data, which enables the model to distinguish areas of high locust risk more effectively than traditional presence-only approaches. The latter are more prone to conflating sampling effort with environmental constraints (Guillera-Arroita et al., 2015). Furthermore, our model operates at a scale of 12 million km², surpassing the spatial scope of most existing studies, which are often confined to smaller areas (Mitra et al., 2024). The forecasting system methodology involves acquiring and processing remote sensing data, unifying environmental data from diverse sources, and linking it to ground survey data for implementation of random forest models. The specific objectives of this forecasting system are.

1. To identify environmental risk factors influencing desert locust distribution by leveraging MODIS satellite data, rainfall datasets, and soil texture and ecoregion maps.

2. To train random forest models using a combination of static and dynamic variables that yield high predictive power for locust occurrence.

3. To provide forecasting maps estimating the probability of desert locust presence at a 1 km² resolution during the recession period.

4. To update predictions every ten days via an automated web interface, ensuring timely and actionable insights for field teams.

5. To validate the system through field surveys, correlating predicted probabilities with observed locust densities.

This study advances locust management by integrating state-of-the-art remote sensing data, machine learning techniques, and operational tools into a unified framework. Our goal is to enhance early warning systems in the western recession area of the desert locust and provide a practical tool for guiding field teams. By generating more accurate and timely distribution maps, it aims to safeguard food security and reduce the environmental impact of locust control in this highly vulnerable region.

## 2. Materials and methods

### 2.1. *Study area and commissions for controlling desert locust*

The Food and Agriculture Organization of the United Nations (FAO) established three commissions to coordinate efforts in researching, monitoring, preventing, and controlling locust populations. The first Commission established in the Eastern region in 1964, the South-West Asian Commission (SWAC) focuses on coordinating locust control in Afghanistan, India, Iran, and Pakistan. The commission for the Central Region (CRC) was initiated in 1994, the CRC coordinates efforts in 16 countries in Central Africa and the Middle East. Lastly, the Commission for Locust Control in the Western Region (CLCPRO), founded in 2002, focuses on implementing preventive desert locust management in 10 West African countries, Algeria, Burkina Faso, Chad, Libya, Mali, Mauritania, Morocco, Niger, Senegal, and Tunisia. Our study was anchored in this CLCPRO region characterized by Mediterranean coast area, Saharo-mediterranean steppes and woodlands, Sahara Desert, Sahelian savannas and tropical sudano-sahelian savannas (Duranton and
Lecoq, 1990). By covering more than 10 million of km², the region plays a pivotal role in addressing locust-related challenges and ensuring the safety of food supplies in West and North Africa hosting approximately 211 million people (CIA, 2023).

## 2.2. Locust observations from ground survey

In 2000, the FAO's Locust group introduced eLocust, a platform on a portable device used for real-time data recording and transmission in the field (Cressman, 2008). This addressed the challenge of incomplete and delayed data transmission. Since then, the desert locust ground surveys follow a standard operating procedure which provides concise instruction for efficient and secure monitoring and data archiving using this platform (FAO, 2020). The first step of the procedure consists in determining by the Locust information Officer where and when survey is needed based on information from different sources (nomads, villagers, travelers, militaries) combined with information on rainfall and habitat. Once the monitoring route is planned, the survey officers are asked to drive along the designed itinerary and to stop on several random sites, most of the time based on changes of desert locust habitats. In each stop, the survey officer made several foot transects (100 m²) to evaluate the presence of locust and to assess the ecological conditions (vegetation and soil moisture). If a significant locust population is already present, they make a more intensive search survey to estimate the total density and occupation area and examine whether control is required and give a forecast of the possible evolution of the situation in short term.

The FAO locustHub database served as our primary source of ground survey observations, synthesizing data collected by surveyors who followed the standard procedure gathered by the eLocust device (See supplementary materials S1). We selected the data collected by the CLCPRO member countries and partitioned the region into a 1 by 1 km grid of 12 861 240 cells on which observation data was overlaid. The pixel dimension aligned roughly with the daily distance a desert locust nymph can cover and a little more than the distance a field agent can make at each site visit (Coppen, 1999; Uvarov and Centre for Overseas Pest, 1977). Data were further aggregated into 10-day periods to facilitate linkage with variables from satellite imagery, aligning with the monthly (3 decades) forecasts desired by field experts. These observational data were used to represent the model's response variable, indicating desert locust presence (1), regardless of phase and stage, or absence (0). Due to the lack of absence records in the FAO locust tables, we used the Ecology table and Control Operation tables to define absence points, assigning the coordinates of these non-detections that lacked observations in the presence-only database but had available habitat descriptions or records of controlling visits. In the context of preventive management, identifying environmental factors influencing locust dynamics before gregarization is crucial. As these factors are different once the population is swarming (Lawton et al., 2022), we removed the years of last locust invasion in the region

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Distribution of survey points from 2003 to end of 2020 across six ecoregions of the CLCPRO area. Red dots represent sites with presence of locusts and black dots display sites where no locust was detected during a 10-day time interval. Ecoregions are extracted from Duranton and Lecoq (1990), with numbers standing for: 1, Mediterranean zone of invasion of gregarious; 2, Saharo-mediterranean zone of remission; 3, Sahara desert; 4, Saharo-sahelian zone of remission; 5, tropical Soudan Sahelian zone of invasion; 6, Meridional zone of gregarious dispersion.</figcaption></figure>
(2004–2005), and multiple groups detections within a 50 km radius were aggregated into a single observation. The resulting database for subsequent analyses consisted of 94 692 aggregated points collected from 2003 till end of 2020, including 34 548 aggregated observations of the desert locusts (Fig. 1).

## 2.3. Environmental data from remote sensing

### 2.3.1. Terra-MODIS satellite and meteorological data

We used well-established MODIS (Moderate Resolution Imaging Spectrometer) satellite imagery sources, acknowledged for their pivotal role in locust ecology (Piou et al., 2013, 2019). For land surface temperature (LST), we utilized the daytime layer from the Terra-MODIS daily product (MOD11A1), version 6.1, spanning from 2000 to the present. (Wan et al., 2021). For the normalized difference vegetation index (NDVI), we used the Terra-MODIS daily 500m reflectance product (MOD09GA), version 6.1 (Vermote and Wolfe, 2021). We used the near infrared and the infrared surface reflectance layers to compute the NDVI for each timestep at each point. Additionally, rainfall data from CMORPH (Joyce et al., 2004) was incorporated in our models. We used the CMORPH data distributed by the International Research Institute for Climate and Society (IRI), at 1 km and 10-days resolution.

The management time scale for reporting and planification is ten days. Hence, we standardized all information sources at the rainfall data resolution (1 km every ten days). To do so, we calculated the max NDVI and max LST on a 10-day basis from the daily products. Aligning data spatially and at consistent intervals with the observation variables ensured that training algorithms were operating on coherent datasets, enabling a more efficient tracking of locust dynamics and easily supporting decision making in the field. To align with the unique characteristics of desert locust biology in arid environments, we accounted for these three environmental predictors dynamically. We considered the critical time for an individual locust in the hopper stage, which was a maximum of 95 days under adverse environmental conditions (Symmons and Cressman, 2001). As our model did not account for the stage or phase of each locust sighting, we established a time frame from 11 days up to 100 days (i.e., nine decades) before the recorded date for each presence to delineate environmental conditions that likely favored locust development at all stages. Using the extract function from the raster package (Hijmans, 2023), we extracted predictors values at each location associated with locust presence or absence. Each data point was linked to 9 values of NDVI, LST and RAIN (denoted NDVI.2 standing for 11–20 days before observation, up to NDVI.10 standing for 91–100 days before, from LST.2 to LST.10 and from RAIN.2 to RAIN.10), capturing the historical evolution of each environmental variable on a decade basis. This approach allowed us to determine time threshold for the environmental conditions to explain locust distribution (Piou et al., 2019, Pekel et al., 2011). We did not consider the first decade before the observation for our model exploration as all satellite images corresponding to the previous decade are rarely available. There is often a delay between the day of satellite scanning and the downloading day of an image from platforms.

### 2.3.2. Other dynamic variables

Because locust activity was not randomly distributed over time, observations were spatially correlated with themselves at different lags. To statistically account for spatial autocorrelation, a variable was created to keep track of the number of desert locust detections within a 100 km radius in the month preceding each observation. In a preliminary analysis, this variable was identified as important—understandably so, given that locust presence can exhibit temporal and spatial clustering. However, the predictive performance of this model, as measured by AUC, was equivalent to that of our original model. Therefore, we decided to remove this covariate, as it cannot be easily updated automatically or integrated into the operational software (See supplementary materials S1).

We also examined the importance of additional explanatory variables, namely Net Primary Production (NPP) and Soil Moisture. The net primary vegetative production from FAO, available every 10 days on a 1 km resolution quantifies the amount of carbon dioxide converted into primary biomass through photosynthesis, providing critical insights into ecological conditions conducive to the spatial distribution of desert locusts (Cramer et al., 1999). Soil moisture images (SM), provided by the SMELLS project as in Piou et al. (2019), were extended for the entire CLCPRO zone and updated until end of 2020. Calculation of the soil moisture product was improved with the latest version of the Dispatch algorithm (Merlin et al., 2013; Ojha et al., 2021). Nevertheless, owing to the data's availability exclusively from 2010 onwards, the study period during which the models were trained for this scenario was inevitably restricted. These models—considering more explanatory variable (i.e., adding NPP and SM) but trained on less historical acridian observations—revealed no marked disparity in predictive capacity (see supplementary materials S1). Therefore, models based on variables that are consistently available, openly accessible, and sustainable over time, leveraging the transition from MODIS to VIIRS. These variables include NDVI, LST, and Rainfall, along with ground survey data spanning from 2003 to 2020.

### 2.3.3. Static variables

To capture the effects of habitat with permanent vegetation or permanent bare soil, we mapped the minimum and maximum NDVI values per sites over the study period. Soil sand content plays a pivotal role in locust ecology (Maeno et al., 2021). It is acknowledged from the locust ecology that a certain amount of sand is needed for females to deposit egg pods in the soil (Popov, 1958). Sandy soil often create spatially and temporally uneven resource distribution conducive to locust adaptation. Moreover, soil sand content can influence soil temperature and moisture conditions, critical for the laying and the survival of locust eggs. Recognizing its importance, we introduced soil sand content, 5–15 cm deep, corresponding to the depth of the egg-laying and plant root development, from ISRIC (International Soil Reference and Information Center) as an explanatory variable in our models (Ellenburg et al., 2021). In addition, our analysis considered eco-climatic zones defined as ecologically homogeneous territorial units and further called ecoregions (Duranton and Lecoq, 1990). These ecoregions provided valuable insights into the ecological context influencing locust distribution and were integrated as static variables in our forecasting approach (Fig. 1).
## 2.4. Model design and implementation

### 2.4.1. Random forest

The development of our model relied on the random forest algorithm, a supervised-learning technique suitable for processing extensive datasets characterized by spatially and temporally correlated predictors, commonly encountered in the analysis of remote sensing data (Song et al., 2021). Renowned for its ability to manage non-linear relationships between predictors and outcomes, random forests demonstrate proficiency in both classification and regression tasks (Breiman, 2001). In the random forest setting, many trees are constructed using randomly selected training datasets and random subsets of predictor variables for modeling outcomes. This allows to maintain the independence between the trees, hence preventing from overfitting noisy data and enabling to get more precise outcomes from majority voting. Using the randomForest package (Breiman, 2001), we applied this algorithm to the prepared dataset composed of 16 years of acridian observations (2003–2020) associated to static features (sand, ecoregion, min and max NDVI) along with the dynamic variables (NDVI, LST, RAIN).

### 2.4.2. Training versus testing data

To ensure the model's ability to generalize and accurately predict outcomes for new data, we divided our current dataset into two distinct parts: training data composed of 80 % of the initial dataset and test data holding the 20 % remaining part. Training data was used to fit the model, balanced towards the minority class to reduce potential bias towards locations where locusts are known to be present, and hyperparameters were adjusted to maximize performance on these data, while test data was employed to evaluate models' performance on new data considered as unseen. Specifically, when training random forests with time series, we considered the temporal dependence between data points was crucial. Therefore, we split the dataset chronologically for training/testing to ensure the model generalized correctly to future data (Kraus et al., 2020). To investigate the sensitivity of model performance to the chronological timing of splitting, we considered 5 scenarios with a splitting at different time window for the 20% test data set. In the first scenario, the test set was from 2003/04/21 to 2007/12/11, with the training set using the remaining years. In the second scenario, the test period was 2007/12/21 to 2010/12/01, and the training set included data from before 2007/12/21 and after 2010/01/11 until 2020/12/21. The third scenario tested on 2010/12/11–2013/01/11, with training on 2003/04/21–2010/12/01 and 2013/01/21–2020/12/21. The fourth scenario tested 2013/01/21–2016/10/11, with the preceding and following periods as training data. The final scenario used all data before the tenth of October 2016 for training and the period from 2016/10/21–2020/12/21 for testing. This chronological cross-validation ensured model generalization, avoiding data leakage. Thus, the model is evaluated across different temporal contexts, capturing environmental variability and locust dynamics.

### 2.4.3. Training the model

Firstly, we conducted a variable selection process based on the recursive feature elimination method aimed to reduce complexity and identify the most informative covariates among those available on a real-time basis, which was crucial for model operatibility. We used the "rfe" function, from the caret package using cross-validation method with 10 folds repeated 3 times, based on the full model (Kuhn, 2022), which accounted for 31 predictors representing habitat (ecoregions and structure with Min and Max NDVI) and soil texture (sand at 5–15 cm depth) and the dynamics of NDVI, LST and RAIN. We further optimized model results, by tuning hyperparameters using the tidymodels package (Kuhn and Wickham, 2022). Among the top 10 combinations of hyperparameters, we selected the one that would subsequently allow us to explore model sets, specifically operational within a reasonable computing time. Therefore, we opted for the hyperparameters: mtry = 19 (19 variables randomly), a minimum size of terminal nodes min_n = 13, and a number of trees n_tree = 1391. This ensures that all trees were independent and sufficiently deep, but with enough data in the terminal nodes, enabling the exploration of more streamlined models without the need for further hyperparameter optimization. For each data splitting scenario, we trained 17 models, in an exploratory multi-model testing approach: models where we removed, each static variable, or one of the three dynamics ones (NDVI, LST, RAIN), models where we remove short-term dynamics of all features (from the 2nd to 4th decades), or where we removed long-term feature dynamics (removing from the 7th to 10th past decades).

### 2.4.4. Evaluation and model selection

We further evaluated the models by predicting outcomes from the features of the test data set and compare predictions with the presence/absence test data. We used the Area Under the Curve, or AUC to evaluate and compare the predictive power between the 17 models from the 5 split scenarios. AUC can vary between 0 for imperfect classification, 0.5 for random classification, and 1 for perfect classification. The predictive power of a model was considered acceptable when its AUC was greater than 0.7 (Mandrekar, 2010; Valavi et al., 2022). Therefore, we chose this threshold to select the models that would be used for forecasting ensuring an acceptable predictive power across all data splitting scenario. For each scenario, we further assessed the average predictive performance by comparing the presence/absence test data with the mean predictions generated by the ensemble models. Finally, we also determined the sensitivity, i.e true positive rate, from the out of bags confusion matrix of balanced random forest models fitted on the full dataset.

### 2.4.5. Variable importance and effects

We represented the rank of variable importance using the mean decrease in accuracy, a feature permutation approach for classification methods robust to correlated variables (Nicodemus, 2011). Additionally, we visualized the effects of the five best predictors on the probability of presence, averaged over small intervals, using Accumulated Local Effects (ALE) plots from the ALEPlot package, also particularly suitable for correlated predictors (Apley and Zhu, 2020). We also showed the response curves, obtained from the spatialRF package (Benito, 2021), and displaying the relationships between probability of presence and each of the predictors in the
supplementary material S1.

## 2.5. Forecasting system overview

Our forecasting system was organized in three steps: 1) data processing, 2) random forest model running and 3) forecast map creation and dissemination. All these steps were automatically run using the cron command line utility on a linux server. This tool allowed to schedule one or more jobs at a chosen time or frequency. The remote sensing data downloading tasks were run daily until data for a whole decade was available. The random forest model and map generating tasks were only run once every 10 days, on the day prior to the start of a new decade, i.e. on the 10th, 20th and the last day of every month.

### 2.5.1. Data processing

Three separate tasks were scheduled in the cron for the remote sensing data processing, one per datatype (LST, NDVI, RAIN). For the two MODIS products (LST and NDVI), raw data was extracted using AppEEARS (Application for Extracting and Exploring Analysis Ready Samples), a NASA data portal that allowed time and space sub-setting. The remote sensing data was requested for 10-days periods on a bounding box area covering the domain of study (CLCPRO countries). Once extracted, the decade maximum value was computed at each point. For the IRI product (RAIN), the decadal sum was readily available and downloaded from the IRI Data Library.

Once downloaded locally, these raw products were processed to fit our needs: data was resampled at 1 km resolution on the same reference grid and clipped on the domain of interest.

### 2.5.2. Random forest model

Among the 17 trained and tested models, we selected 3 best models with informative covariates that were available on a real-time basis and that allow to forecast for three successive decades. These selected models were fitted one last time to the full dataset (joining back the train and test datasets) for the operational version of the model. We also ensured that their sensitivity was adequately high to avoid the risk of incorrectly predicting an absence. Presence/absence outcomes were then predicted over the study area based on the newly available remote sensing data, and using the results of the 3 best models (See result section and Supplementary materials S1).

### 2.5.3. Forecasting map

We used the selected models to predict the probability of presence of desert locust in each site of the full study area based on new satellite image updated and downloaded every ten days in an automated process as explained in supplementary materials S1. Subsequently, we produced forecast maps in remission zones and categorize probability of presence into 6 classes (≤50%, >50%, >60%, >70%, >80%, >90% chance of occurrence). This comprehensive approach allowed us to incorporate the evolving information into the forecasting process, providing more accurate and nuanced predictions that account for recent developments and environmental changes. Forecasting maps were shown in an html page generated using the leaflet R-package (Cheng et al., 2024). This page was hosted on Github Pages and displayed the forecast maps for 6 decades: the map for the current decade and maps for 2 following decades for orientation during field survey, along with maps for the previous 3 decades for a fast and easy retrospective check of the model predictions by the National centers for locust control (https://github.com/pioucyril/mppcpro).

## 2.6. Field evaluation

To evaluate the model in the field, we planned a survey route in collaboration with the Mauritanian National Anti-locust Center based on the forecasting map for the first decade of December 2023. We aimed to investigate three specific zones of interest for locust presence. These zones included 1) the north-northeast of Bou Sdéra, where the forecasting system predicted a probability of over 90% of finding desert locusts, 2) the wadi between Akjoujt and Benichab with a similar high probability, and 3) an area where locust control were conducted in November 2023 on hopper and adult groups, approximately 50 km northeast of Nouakchott. A total distance of 1343 km was covered during the survey and we made 58 observations. Each day, the major sectors to be visited were determined based on model forecasts, and more precise routes and stopping points were chosen considering both the model predictions and the practical terrain accessibility. The OSMAND © smartphone application (https://osmand.net) was used for real-time location tracking in relation to forecasts. The goal was to verify model predictions, and selected points for investigation covered a range of probability classes, ensuring at least five survey points in each class (from≤50%, to >90%).

Surveys were conducted from 08:00 to 18:00. At each survey point, three surveyors independently assessed the locust situation, recording the number of desert locusts encountered, the distance covered and the encountered vegetation. Each surveyor covered at least 400 m² on foot (in high locust density areas), with 100m-transects over 2 km. The three survey officers determined the direction
<caption>**Table 1**</caption>

Models predictive power depicted by AUC for the test data sets ACROSS 5-splitting scenarios and by the sensitivity for the full data set. Model names refer to the missing covariate (see supplementary materials S1 for more details). Finally, the ensemble line shows the area under the curve values of the ROC functions calculated from the average predictions of all the models stated above. Grey cells are the models excluded from our criterion selections (AUC <0.7).

<table><thead><tr><th>Models name</th><th>2003-04-21/2007-12-21</th><th>2007-12-21/2010-12-11</th><th>2010-12-11/2013-01-21</th><th>2013-01-21/2016-10-21</th><th>2016-10-21/2020-12-21</th><th>Full data</th></tr></thead><tbody><tr><td>modnoObs1</td><td>0.765</td><td>0.795</td><td>0.755</td><td>0.728</td><td>0.740</td><td>0.754</td></tr><tr><td>modnoObs2</td><td>0.759</td><td>0.790</td><td>0.751</td><td>0.721</td><td>0.734</td><td>0.751</td></tr><tr><td>modnoObs3</td><td>0.754</td><td>0.787</td><td>0.744</td><td>0.716</td><td>0.730</td><td>0.751</td></tr><tr><td>modnoObs7</td><td>0.745</td><td>0.769</td><td>0.743</td><td>0.723</td><td>0.723</td><td>0.735</td></tr><tr><td>modnoObs8</td><td>0.754</td><td>0.778</td><td>0.745</td><td>0.729</td><td>0.724</td><td>0.739</td></tr><tr><td>modnoObs9</td><td>0.759</td><td>0.787</td><td>0.748</td><td>0.729</td><td>0.729</td><td>0.746</td></tr><tr><td>modnoObs10</td><td>0.763</td><td>0.792</td><td>0.752</td><td>0.728</td><td>0.737</td><td>0.749</td></tr><tr><td>modnoMinMaxnoObs1</td><td>0.755</td><td>0.777</td><td>0.730</td><td>0.698</td><td>0.735</td><td>0.761</td></tr><tr><td>modnoMinMaxnoObs2</td><td>0.744</td><td>0.768</td><td>0.720</td><td>0.691</td><td>0.721</td><td>0.759</td></tr><tr><td>modnoMinMaxnoObs3</td><td>0.732</td><td>0.760</td><td>0.709</td><td>0.680</td><td>0.710</td><td>0.752</td></tr><tr><td>modnoEcoregion</td><td>0.768</td><td>0.790</td><td>0.749</td><td>0.714</td><td>0.735</td><td>0.731</td></tr><tr><td>modnoSand</td><td>0.760</td><td>0.791</td><td>0.753</td><td>0.722</td><td>0.744</td><td>0.741</td></tr><tr><td>modnoLST</td><td>0.768</td><td>0.783</td><td>0.749</td><td>0.742</td><td>0.737</td><td>0.744</td></tr><tr><td>modnoRAIN</td><td>0.747</td><td>0.785</td><td>0.749</td><td>0.730</td><td>0.728</td><td>0.733</td></tr><tr><td>modnoNDVI</td><td>0.726</td><td>0.746</td><td>0.713</td><td>0.664</td><td>0.696</td><td>0.769</td></tr><tr><td>modDynamonly</td><td>0.769</td><td>0.789</td><td>0.744</td><td>0.710</td><td>0.742</td><td>0.732</td></tr><tr><td>Ensemble</td><td>0.773</td><td>0.798</td><td>0.751</td><td>0.719</td><td>0.748</td><td>0.743</td></tr></tbody></table>

of the survey from the starting point where the vehicle stop and coordinated to avoid overlapping coverage. Upon returning to the vehicles, the total distance covered (Dist) and the total number of observed desert locusts (N) were used to calculate local density, considering each survey officer had a vision of approximately 10 m around their walking routes. Thus, density (D) was calculated simply as: D = N/Dist (where Dist is in km, and D is in individuals/ha).

# 3. Results

## 3.1. Evaluation on test data

The ensemble models approach revealed the greatest predictive power (AUC = 0.759 ranging from 0.719 to 0.798) (Table 1). Among the 17 models trained for each of the 5-splitting scenario, the best time split window for the test dataset was between 2007 and 12–21 and 2010-12-11, following the invasion years removed from the study period. The models that excluded all NDVI variables (modnoNDVI in Table 1) did not achieve a sufficient predictive score to be considered. Models that excluded the minimum and maximum NDVI (modnoObsnoMinMax in Table 1) performed only for the testing years [2013/01/21–2016/10/21[ and showed reasonable performance for other data partitions. The model with the best predictive power among single models was the one including all predictors (modnoObs1, mean AUC = 0.757 [0.728–0.795] and sensitivity of 0.754). We further used this model for importance and accumulated local effects of most informative variables.

## 3.2. Variable importance

Among the 31 predictors included in this study, the two topmost informative ones were static variables, the homogenous ecological unit with regard to desert locust biotope and the proportion of sand content in the soil between 5 and 15 cm depth (Fig. 2). The following most important variables were land surface temperature and vegetation cover NDVI on a short term (two decades before) along with the level of rainfall with intermediate delay (5th decay). Notice that the recursive feature elimination did not remove any of the 31 variables suggesting that all variables matter.

## 3.3. Local accumulated effects of predictors

Among the 6 ecoregions defined as recession zones of the desert locust, the Saharo-sahelian zone appeared to explain locust presence the most. Soils containing more than 55% sand at a depth of 5–15 cm had a positive effect on locust presence (Fig. 3A and B). The local effects of dynamic variables specifically indicated a positive relationship between short-term land surface temperature (LST.2) and desert locust presence, with positive effects observed above a threshold of 42 °C (Fig. 3C). We observed a reverse trend in the effect of LST depending on the time scale: short-term LST positively affected presence probability, while long-term LST exerted a negative impact (Fig. S1.3A, F-H). Additionally, rainfall from five decades prior, when accumulated above 8 mm, was found to favor desert locust presence (Fig. 3D). In the short term, NDVI from the second previous decade (NDVI.2) positively influenced presence probability, reaching a plateau at about 0.20 corresponding to intermediate level of vegetation cover (Fig. 3E). In contrast, long-term NDVI exhibited a negative impact on locust presence probability. Response curves revealed that sites characterized by bare soil and low vegetation levels in earlier decades (beyond the seventh) were associated with higher locust presence (Fig. S1.5H-J).
<figure><img src="image_6.png" /><figcaption>Fig. 2. Mean decrease in the best model accuracy when variables permuted. From the top to the bottom, it shows in order the most informative variables of the best model modnoObs1.</figcaption></figure>

## 3.4. Selected model for operational forecasting system

Among the 17 models trained for each of the 5-splitting scenario, the final selection resulted in 3 models with informative covariates and available for real-time forecasts which was crucial for model operatibility (Table 1). ModnoObs1 included all predictors which allowed in the last day of a given decade to make a forecast of the coming decade based on the MODIS images from the second to tenth last decades, without using the preceding decade not yet available. ModnoObs2, by removing NDVI.2, LST.2, and RAIN.2, allowed to forecast the desert locust distribution two decades forward. By following the same logic, ModnoObs3 enabled forecasting one month in advance (Supplementary materials for more details, Fig. S1.8). Despite the good predictive power of the ensemble model, we decided not to use an averaged approach for the forecast as models including with the second and third last decades are nested in this ensemble model, preventing to make forecast 20 days to one month in advance.

## 3.5. Evaluation on field data

In the initial period of December 2023, our model, employed to orient the survey route and choose survey points, demonstrated an effective power in forecasting presence sites (Fig. 4). Out of the 44 visited sites with expected locust presence, locusts were observed at 34 sites, resulting in a 77% success rate in predicting presence (Fig. 5), which aligns with the sensitivity of the model predictions (Table 1). Of the 14 sites predicted to have no locust presence, only two were incorrectly classified as false negatives (Figs. 4 and 5). Following observations in the areas north-northeast of Bou Sdéra and north of Nouakchott, we found a positive correlation observed densities against locust probabilities predicted by the forecasting tool. We did not analyze this relationship statistically as prospection points were not sampled randomly but oriented by the model predictions (Fig. 5).
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Accumulated Local Effect plots for the 5 most important variables. The x axis represents the range of values for each predictor. Distribution of real values are represented as a rug plot on the x-axis. The y-axis represents the effect on presence probability for each value of the predictor. ALE >0 indicate a positive effect of the variable on the presence of Desert locust, while ALE <0 informs on a negative effect of the predictor.</figcaption></figure>

# 4. Discussion

## 4.1. *Insights and contributions of the model*

Our study enhances the understanding of desert locust ecology and presents a robust operational forecasting tool. Unlike simpler models based on singular predictors like NDVI (Lawton et al., 2022), Soil Moisture (Ellenburg et al., 2021) or their combinations but without temporal dynamics (Saha et al., 2021; Kimathi et al., 2020; Cornejo-Bueno et al., 2023), our random forest model integrates dynamic variables, capturing recent and past variations in vegetation, land surface temperature, and rainfall. This approach provides real-time, updated forecasts and deeper insights into locust presence probabilities, offering a more comprehensive preventive framework compared to reactive models making long-term projections based on static averaged variables. Our desert locust forecasting system, developed for the western region of the recession area, has shown great success in the field. There was a correlation between the predicted probabilities and observed locust densities. This accurate forecasting of high locust concentration areas supports its potential as a decision-support tool for survey teams, though further field evaluation is required before full deployment. The extension of our study to produce forecasts at a large regional scale fills a crucial gap in existing literature, as previous research using remote sensing and machine learning to provide real-time forecast of desert locust presence have primarily focused on national studies rather than comprehensive regional assessments and were almost never transformed into an operational tool (Anyamba and Tucker, 2005; Hielkema et al., 1986; Piou et al., 2013, 2019; Sun et al., 2022; Gómez et al., 2019).

Our study supports previous research linking locust presence to arid habitats with rapid vegetation growth following precipitation events (Piou et al., 2013). Past precipitation emerged as a key predictor in our model, emphasizing its role in locust habitat suitability. In arid regions, rainfall triggers vegetation growth, creating favorable conditions for locust development. Land surface temperature also plays a dual role: high short-term temperatures likely indicate a recent shrinkage of vegetation patches, favoring locust densification, while lower long-term temperatures likely reflect stable soil moisture levels critical for egg laying.

One notable aspect of our study is the integration into the model of eco-regional data and soil sand content underscoring their role as key predictors of locust distribution. For instance, sandy soils with optimal sand content are essential for egg laying (Ellenburg et al., 2021). Moreover, the Saharo-sahelian region emerges as the well-known hotspot for locust presence, emphasizing the importance to maintain constant surveillance in this area within a cross-border early warning and information system piloted by the CLCPRO. This ecoregion spans 6000 km across North Senegal, Southern Mauritania, and central Sudan, characterized by erratic summer rainfall
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Visualization of a part of the forecasting map with a color gradient ranging from blue, for probability of presence between 0.5 and 0.59 to yellow for probability of presence above 0.9. The map is zoomed on Mauritania, the prospected area selected for field evaluation. Visited sites filled in red indicate presence of desert locust at increasing density as the color gets more pronounced. Empty diamonds state for visited sites where no locust was detected.</figcaption></figure>

driven by the Intertropical Convergence Zone and diverse landscapes of sandy desert, shrublands, acacia forests, and savannas (Naia and Brito, 2021). This result aligns with the acknowledged understanding in locust ecology that the Sahara-Sahelian biotopes favor locust dispersion and reproduction, particularly of solitarious populations scattered all along this zone during the remission period (Duranton and Lecoq, 1990).

## 4.2. Spatial autocorrelation, scales and sampling bias

Due to their high social aggregation behavior, desert locusts are often found in clusters, leading to spatial autocorrelation in the data. This means that the probability of detecting locusts in the field depends on their recent local density. This is particularly important for forecasting the distribution of gregarious individuals in the field, although it was not the main focus of our study. While advanced machine learning methods could incorporate spatial autocorrelation directly (Benito, 2021; Sigrist, 2022), these approaches are computationally intensive and challenging to operationalize on the scale required for our forecasting tool. We use an ad-hoc approach to account for spatial autocorrelation, by including a covariate recording the numbers of locust observation points collected on a 100 km buffer range in the month preceding each prospection points. While preliminary analysis revealed that such spatial autocorrelation was an important predictor to explain locust presence, incorporating this data in real-time forecasting presented a challenge. This difficulty stemmed from delays between field teams reporting their survey and control results to decision-makers, who are often located far away at the national capital (Cressman, 2008). Our models excluding the spatial features of previous monthly observations, still performed well in forecasting locust presence up to one month in advance and therefore were selected for operational purpose. We also found robustness in the predictive power of our models, regardless of the spatial resolution we used for the forecasts (250 m or 1 km). We therefore define the model structure, scale, and forecasting horizon, attempting to balance the trade-offs between model complexity, data availability, and practical relevance concerning locust biology and monitoring constraints. Our approach ensures the development of effective forecasting models for timely and reliable locust management interventions on a realistic scale, using only the essential information that can be sustainably obtain in the future.
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Relationship between forecast classes and observed desert locust densities during the field validation survey in 2023 from December 03 to 10. TP, True Positives. TN, True Negatives.</figcaption></figure>

Another limitation of our approach, is that it does not do not dissociate observation processes from ecological dynamics. While our models adjust for imbalanced sampling, they are still limited by the spatial autocorrelation in monitoring effort (Rota et al., 2011). Instead, it offers a pragmatic solution for guiding field teams to areas with high locust detection probabilities based on monitoring history and environmental conditions, rather than inferring true species distribution. Areas predicted as low risk do not imply locust absence, as heterogeneity in sampling effort is not fully accounted for. While the true negative rate was high (0.88), its relevance is reduced by potential sampling biases. Future work could incorporate mechanistic models that integrate locust demography, migration, gregarization, and imperfect detection (Sorel et al., 2024; Veran et al., 2015; Herbillon et al., 2024). Such integrative approach would be suitable for incorporating both climate change projections and management strategies into decision-making processes to safeguard agricultural productivity and food security in locust-prone regions.

### 4.3. Strength and perspective for preventive and adaptive management

The Desert Locust Information Service (DLIS) at FAO currently employs forecasting tools like the Shistocerca WArning Manage- ment System (SWARMS), which integrates meteorological data, wind patterns, survey data, historical records, and remote sensing imagery to predict locust movements and breeding sites (Cressman, 2008). These mechanistic models, such as the Desert Locust Egg and Hopper Development Model (Pedgley, 1981) and the Trajectory Model (Cressman, 2008), are designed for reactive management, focusing on medium- and large-scale swarming areas (Roffey and Magor, 2001). By contrast, our machine-learning-based forecasting tool emphasizes prevention during recession periods, predicting the distribution of solitarious and transiens locusts. This upstream approach identifies areas with high probabilities of locust presence and potential phase transitions, complementing SWARMS, which is better suited for large groups or marching bands.

The transition of our tool from a prototype to an operational system represents a milestone for preventive management in the CLCPRO region. Its development involved collaboration among researchers, field experts, and intergovernmental organizations, showcasing the value of co-designed scientific and technological programs (Word Ries et al., 2024). However, the success of its implementation depends on effective training of national surveyors and the adoption of standardized protocols for use and evaluation. While information predominantly flows from field teams to management centers, enhancing top-down guidance through training remains critical. Challenges related to governance and intergovernmental coordination, including the prioritization of quantifiable objectives and timelines, persist. These uncertainties also impact decision-making, monitoring protocols, and our ability to apprehend locust capacity to adapt to climate change, limiting the achievement of objectives (Piou and Marescot, 2023; Williams, 2011).

Adaptive management offers a robust framework for addressing these challenges by iteratively adjusting strategies based on new
information and past outcomes (Williams, 2011). Incorporating adaptive feedback loops into the forecasting system would allow for continuous evaluation of the models' performance through ongoing monitoring and updating our beliefs in the ability of the system to inform decision strategy (Chapman et al., 2023). For instance, historical management data—such as the timing, intensity, and types of control measures (e.g., chemical pesticides, physical trap, pheromones, genetic, or biological control)—could be integrated into the random forest models to improve predictions and adapt future actions (Lecoq, 2001; Shuang et al., 2022). This iterative process, akin to trial-and-error learning, could eventually support retraining of machine learning models with expanded datasets, enabling more accurate forecasts and better-informed management decisions. Unlike traditional decision-making frameworks such as Markovian processes (Shea et al., 2002), this heuristic approach would not require explicit modeling of state transition probabilities or maximization of expected rewards (Marescot et al., 2013). Instead, it leverages machine learning to classify critical thresholds and minimize risks of exceeding them (Cissé et al., 2015). A "data-based" strategy prioritizing forecasting over explicit system modeling could streamline decision-making, especially if integrated into reinforcement learning frameworks (Chapman et al., 2023).

# 5. Conclusions

In summary, our forecasting system combines methodological rigor with practical utility, offering a scalable and effective tool for locust preventive management across the CLCPRO region. By integrating habitat predictors with dynamic climatic and vegetation data, our model advances the understanding of locust ecology while meeting operational needs. Field evaluations have confirmed its potential to guide resource allocation effectively, though further assessments are needed to evaluate its long-term impact on mitigating locust remission. Future research should continue to refine this approach, exploring the integration of advanced spatial modeling techniques and adaptive management principles to further enhance its utility in a changing environment.

## CRediT authorship contribution statement

**Lucile Marescot:** Writing – original draft, Visualization, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Elodie Fernandez:** Writing – original draft, Visualization, Software, Methodology, Data curation. **Hichem Dridi:** Software, Resources, Project administration. **Ahmed Salem Benahi:** Validation, Project administration, Data curation. **Mohamed Lemine Hamouny:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Conceptualization. **Koutaro Ould Maeno:** Writing – review & editing, Validation, Data curation. **Maria-José Escorihuela:** Methodology, Data curation. **Giovanni Paolini:** Methodology, Data curation. **Cyril Piou:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Software and data availability section

All the raw datasets used in this study were downloaded from open FAO locustHub database available upon request on https://locust-hub-hqfao.hub.arcgis.com/. Remote sensing images are available on the earth data NASA platform upon the creation of an account: https://urs.earthdata.nasa.gov/home. The precipitation data were obtained from the International Research Institute for Climate and Society available on http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.Analyses/.Locust/.CMORPH/.dekadsum/figviewer.html?map.url=X+Y+fig+colors++fig and the soil sand content map from the International Soil Reference and Information Center can be downloaded on https://files.isric.org/soilgrids/latest/data/sand/sand_5-15cm_mean/. The automatic downloading of satellite images and forecasting system along with the scripts needed to replicate the model training are available in the following dataverse directory: https://doi.org/10.18167/DVN1/4IY2TG. The study was conducted using R 4.1.2 (R Core Team, 2022) and Python.

## Ethical statement

This research adheres to all ethical standards as outlined by Elsevier's publishing policies. Informed consent was secured from all participants involved in the study, and their anonymity and confidentiality were maintained throughout the research process.

The authors affirm that the study was conducted following the principles of the Declaration of Helsinki and other applicable international guidelines. There are no conflicts of interest to declare, and the authors have ensured that the research was free from bias and undue influence. Data management, analysis, and reporting were conducted with the highest level of integrity and transparency.

All co-authors have contributed significantly to the work and have approved the final manuscript for submission. There has been no fabrication, falsification, or inappropriate data manipulation, and the research complies with all applicable laws and institutional policies.

## Declaration of generative AI in scientific writing

ChatGPT, a generative AI technology, was utilized in the writing process to enhance the readability and improve the English language of the manuscript. The authors ensured that the AI-assisted edits did not alter the original meaning of the content.
Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing in-
terests:Cyrile Piou and Mohamed Lemine Hamouny reports financial support was provided by Agence francaise pour le
développement. Cyril Piou, Lucile Marescot and Mohamed Lemine Hamouny has patent pending to licence CC-BY-SA. If there are other
authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.

Acknowledgments

This work was conducted under the project “Consolider les bases de la stratégie de lutte préventive et développer la recherche opérationnelle sur le Criquet pèlerin en région occidentale” lead by the CLCPRO (Commission de Lutte Contre le Criquet Pèlerin dans la Région Occidentale) of the FAO (Food and Agriculture Organisation of the United Nations) with funding from the AFD (Agence Française pour le Développement). This work is also part of the work package “Desert Locust Forecasting tool” from the ACCWA project (Accounting for Climate Change in Water and Agriculture management) funded by the H2020 MSCA RISE program. We are grateful to all the field surveyors that collected the locust data across the CLCPRO region and all the CLCPRO-FAO leaders and consultants, such as Khaled Moumene, for organizing regular workshops and meeting with information officers and researchers to make this tool operational. We also thank Pierre-Emmanuel Gay for his contributions to the Dataverse maintenance and FAO reports writing.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rsase.2025.101497.

Data availability

Data will be made available on request.

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## Update

# Remote Sensing Applications: Society and Environment

Volume, Issue, Page

DOI: https://doi.org/10.1016/j.rsase.2025.101539
<figure><img src="image_10.png" /></figure>

ELSEVIER

Contents lists available at [ScienceDirect](https://www.sciencedirect.com)

Remote Sensing Applications: Society and
Environment

journal homepage: [www.elsevier.com/locate/rsase](https://www.elsevier.com/locate/rsase)

<figure><img src="image_11.png" /></figure>

Corrigendum to “A forecasting model for desert locust presence
during recession period, using real-time satellite imagery”
[Remote Sens. Appl.: Soc. Environ. (37) 101497]

Lucile Marescot<sup>a, *</sup>, Elodie Fernandez<sup>a</sup>, Hichem Dridi<sup>b</sup>, Ahmed Salem Benahi<sup>c</sup>,
Mohamed Lemine Hamouny<sup>b</sup>, Koutaro Ould Maeno<sup>d</sup>, Maria-José Escorihuela<sup>e</sup>,
Giovanni Paolini<sup>e</sup>, Cyril Piou<sup>a</sup>

<Note to JM: Errors in article heads, particularly titles, author names, affiliations must not be repeated. In such cases, use the correct title, authors, affiliation(s), etc. If the corrigendum is the result of authorship changes, include the correct author details in the article head.>

The authors regret an automatic error in the stated location of author's affiliation b. "Food and Agriculture Organization of United Nations, Alger, Commission de Lutte Contre le Criquet Pèlerin en Region Occidentale (CLCPRO), France"

The authors would like to apologise for any inconvenience caused.

DOI of original article: [https://doi.org/10.1016/j.rsase.2025.101497](https://doi.org/10.1016/j.rsase.2025.101497).

* Corresponding author.

E-mail address: [Lucile.marescot@cirad.fr](mailto:Lucile.marescot@cirad.fr) (L. Marescot).

https://doi.org/10.1016/j.rsase.2025.101539

Available online 4 April 2025
2352-9385/© 2025 Published by Elsevier B.V.

<footnote><sup>a</sup> CBGP, INRAE, IRD, CIRAD, Institut Agro Montpellier, Montpellier University, Montpellier, France</footnote>

<footnote>b Food and Agriculture Organization of United Nations, Commission de Lutte Contre le Criquet Pèlerin en Region Occidentale (CLCPRO), Algiers, Algeria</footnote>

<footnote>C Centre National de Lutte Antiacridienne (CNLA), Nouakchott, Mauritania</footnote>

<footnote>d Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Japan</footnote>

<footnote>e isardSAT, Parc Tecnològic Barcelona Activa, Barcelona, Spain</footnote>