Prospects for pastoralist-farmer conflict in Africa

Prospects for pastoralist-farmer conflict in Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prospects for pastoralist-farmer conflict in Africa Mostafa Khorsandi, Erwann Fillol, Andrew Smerald, Klaus Butterbach-Bahl, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5860400/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pastoralism is a major way of life in the Sahelian and Sudanian (SaSu) zone of Africa, playing an important social-environmental role through food production and the use of suitable land for seasonal migrations (transhumance). Using Earth Observation (EO) data, we systematically analyze environmental factors—water access, soil properties, topography, vegetation cover, tree cover, road access, and biomass availability— to assess the SaSu’s suitability for transhumance as well as for permanent farming systems, and provide perspectives on potential conflict zones between herders and farmers in case of conflicting interests. Our study is the first to present comprehensive and detailed transhumance corridors that account for environmental constraints. We show that 69% of conflicts from 2001–2020 involve or are related to tensions between farmers and pastoralists, while 31% of conflicts are attributed to interactions between pastoralists. Our research provides valuable insights into the complex relationships between pastoralist communities and their socio-ecological environment and highlights the critical role of EO-based decision support systems in mapping and understanding pastoralism in the SaSu region. Earth and environmental sciences/Environmental sciences/Environmental impact Scientific community and society/Social sciences/Interdisciplinary studies Scientific community and society/Agriculture Scientific community and society/Developing world Earth and environmental sciences/Climate sciences/Hydrology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Historically, nomadic pastoralism has been a key adaptation to the variable environment 1 – 4 and has persisted despite the changes brought about by the Industrial and Green Revolutions, where new approaches of resource use have emerged 5 , 6 . Despite these changes, pastoralism remains a sustainable way of harmonizing human activities with nature 1 , 4 , 7 . Africa, which benefits from the presence of pastoralists for food production while preserving its ecosystems 8 – 13 , faces challenges as human impact on ecosystems increases the struggle for limited resources, endangering pastoralists 8 , 14 – 16 and occasionally leading to conflict 4 , 14 – 19 . In Nigeria alone, between 2016 and 2018, a total of 3,727 deaths and numerous casualties were reported due to conflicts between pastoralists and farmers 20 . The number of casualties is much higher in the SaSu region 15 , 21 . A fundamental step in the sustainability analysis of transhumance in Africa is to assess the suitability of land for this way of life. While localized studies of the feasibility of transhumance exist, such as in Southern Ethiopia 22 , Osun State of Nigeria 23 , Namibia 24 , Republic of Chad, and the Central African Republic 8 , the widespread occurrence of transhumance and the lack of tracking data make Earth Observation (EO)-based methods the most viable method for large-scale studies 8 . While factors influencing transhumance have been discussed in several sources 8 , 22 , 23 , 25 – 29 , EO studies for assessing the suitability of land for transhumance in the SaSu are lacking. Key determinants include water availability, vegetation cover, and their seasonal variations 8 . Net primary production (NPP), derived from satellite imagery, is also a valuable tool for studying land productivity, with applications ranging from agricultural production assessment 30 to food production 31 and rangeland capacity assessment 32 . In SaSu, satellite-based NPP products can help to determine biomass availability for transhumance migration routes around livestock grazing areas 33 . Despite the knowledge of the main environmental drivers of transhumance 10 , 34 , most studies have been limited to a single year or a specific region, thereby potentially overlooking interannual or long-term changes in transhumance activities and pathways. At the same time, EO data and global-scale data sources can be used to detect and assess agricultural land used either by farmers or partially by pastoralists 35 . Moreover, agricultural suitability assessments have been applied in arid and semi-arid regions of the world to identify land for farming 35 , 36 . Agriculture suitability criteria have also been well developed by international agencies 37 , 38 . However, no previous study shows the suitability of agricultural land for SaSu. After pastoralists, farmers are the other major users of natural resources in SaSu for food production. Africa has a high demand for food due to its growing population, which is projected to increase from 9% of the world’s population in 1950 to 35% in 2100 39,40 . The SaSu is no exception, and the population will increase 2.5-fold or higher between 2010 and 2050 period 39 , 41 . However, most of the food demand will be imported into the SaSu countries (e.g., for cereals alone, the current self-sufficiency ratio of 0.79–0.87 will decrease to 0.33–0.48 in 2050) 39 , 42 . There are two methods for meeting the high internal demand for food in SaSu: (1) increasing the cropland area and (2) closing the yield gap between current and potential yields on existing cropland 39 . This increase in cropland area is likely to increase the number of conflicts between pastoralists and farmers in SaSu. Previous studies have shown that research on the causes of conflict between farmer-pastoralist conflict is scarce 43 . The review found a direct link between the conflict and land or natural resources with an increasing trend; however, there was little quantitative evidence to support this 43 . On the other hand, some studies emphasize that governance or social factors cause conflict in addition to resource scarcity/competition 43 . In this regard, more and deeper research on the relationship between resource availability and pastoralist-farmer conflict is emphasized 43 . In our study we developed a geospatial tool to assess the suitability index for transhumance (SI Transhumance ) in SaSu, which allows to narrow down suitable areas into high-resolution transhumance corridors, and the assessment of the suitability of land for cropping systems using the suitability index for agriculture (SI Agricluture ). Based on this approach we identify conflict-prone areas due to competition of pastoralists and farmers using the suitability index (SI) analysis and EO-based data. Thus, our analysis provides an improved understanding of transhumance, in order to facilitate transhumance planning, and identify potential conflict risk areas on the basis of a wide range of EO data. 2. Results The suitability of transhumance and agriculture was assessed by overlaying standardized geospatial layers, categorized into six classes: unsuitable (SI = 0), very poor (0 < SI ≤ 0.2), poor (0.2 < SI ≤ 0.4), medium (0.4 < SI ≤ 0.6), good (0.6 < SI ≤ 0.8), and very good (0.8 < SI ≤ 1). These classifications aim to quantify the potential scale of agriculture-pastoralist conflict. 2.1. Land suitability for transhumance The suitability of land for transhumance was evaluated using an overlapping fuzzy membership function technique of EO data, incorporating six layers, including water, vegetation cover, tree cover, roads, urban areas, and MODIS product. Two main limiting factors for transhumance—water accessibility and vegetated land cover as livestock feed sources—are incorporated in our assessment using monthly layers to capture seasonal variations (Section ‎4.3.1). Results indicate that approximately 21% of the study area (112.1 million hectares) is classified as good or high-quality for transhumance (Fig. 1 ). Validation of the SI Transhumance map was conducted qualitatively by comparing systematically digitized transhumance corridors [Figure 2 (a)], largely derived from pastoralist inputs during collaborative meetings (see Table S1 for sources). While these insights are valuable, their precision is limited due to (1) annual variability in transhumance routes, (2) challenges of representing a sub-continental region within a limited engagement period, and (3) the imprecision of verbal communication. In addition, our study highlights a more spatially detailed SI Transhumance map compared to available knowledge, enhancing the accuracy of corridor delineation across the SaSu region. 2.2. Land suitability for agriculture Agricultural suitability was derived from eight EO layers using variables such as precipitation, soil parameters, and slope. The analysis leveraged a previous study combining six global cropland datasets to identify areas of cropland agreement 44 . This consensus-based layer estimates 185.5 million hectares of cropland in SaSu, while non-croplands with high agreement total 348.3 million hectares (Fig. 3 ). The agricultural suitability analysis focused on agreed croplands (agreement levels 1–6; number of sources identifying the area as cropland). Results reveal that 3.6% (6.7 million hectares) of agreed croplands are unsuitable for arable farming due to low soil quality (low CEC and OC) or water scarcity. The remaining croplands were classified from very poor to very good suitability, with water availability being the primary limiting factor. Approximately 27.2% (50.4 million hectares) of cropland with low suitability (unsuitable or poor) falls in areas with low agreement (levels ≤ 3), indicating seasonal rainfed practices with low yields. Nevertheless, 96.4% (178.8 million hectares) of EO-based croplands exhibit a minimum suitability (SI Agriculture > 0), consistent with the 2020 cropland extent (185.5 million hectares). This agreement confirms the suitability of these lands for agriculture and identifies the maximum potential overlap for pastoralist-farmer conflict. 2.3. Potential zones for pastoralist-farmer conflict The SaSu region is divided into three transhumance suitability zones (Fig. 1 ): areas with SI Transhumance 0.8 show rare transhumance activity (< 5%), while the primary zone for transhumance (95%) lies within 0.25 ≤ SI Transhumance ≤ 0.8 [Figure 1 and Figure S3(c)]. Using cropland agreement classes, 69.3% of conflicts occur in areas agreed upon as cropland, 30.7% occur in non-cropland areas, and 3.4% occur in areas with high cropland agreement. This suggests that the 30.7% in non-cropland areas likely represent pastoralist-pastoralist conflicts, unrecorded croplands, or limitations in the input data [Figures S3(b) and S4]. For pastoralist-related conflicts, zones with SI Agriculture 0.5 show rare occurrences, while the primary conflict zone (0.05 ≤ SI Agriculture ≤ 0.5) is within croplands [Figure S3(d)]. This range aligns with areas of frequent interactions between transhumance and agriculture, leading to higher conflict intensity. Figure S3 demonstrates a high correspondence between the SI Transhumance map and the reported pastoralist conflict locations in Figure S4(a). Figure 4 illustrates hotspots for pastoralist-farmer conflicts, derived from the combination of Fuzzy SI Transhumance and Fuzzy SI Agriculture , highlighting areas most prone to tensions. The validation points for recorded conflicts confirm the conflict hot spots in Fig. 4 . By placing adjacent conflict points in a zone, there are 19 hot spot conflict zones in SaSu. Figure S4 examines conflict pixels reported between 2001 and 2020 and classifies them into six agreement classes based on cropland agreement. The results show that 30.7% of reported conflicts occur in non-cropland areas, suggesting pastoralist-pastoralist conflicts or low accuracy of the agreement layer in discriminating cropland from non-cropland for these regions. Notably, most conflicts, constituting 69.3%, are found in agreement classes 1–6. These agreement classes indicate how many sources identified a pixel as cropland. If a pixel is classified as cropland, it may represent either rainfed or irrigated land. This makes it challenging to determine the second actor in the conflict—whether it is a farmer or another pastoralist—beyond the first pastoralist involved. However, the fact that SI Agriculture > 0 for these lands and that they are reported as croplands, suggests the strong possibility of pastoralist-farmer conflict. Again, by classifying the conflict pixels based on SI Agriculture , almost none of the conflicts are in high quality areas for agriculture [SI Agriculture > 0.8, Figure S3(b)]. On the other hand, almost 15% of the conflicts are located in areas unsuitable for permanent cultivation which are likely to be rainfed and not cropped all year round [Figure S3(b)]. Three of the six agricultural suitability classes (very poor, poor, and medium) serve as common pool resources for both farmers and pastoralists, where competing interests over environmental resource use play a key role in conflicts (Figure S3). 2.4. Pastoralists’ conflict over time To understand the temporal dynamics of pastoralist conflicts, the study period is divided into two sub-periods: 2001–2010 and 2011–2020. The frequency of conflict occurrence and the probability distribution based on the suitability index (both SI Transhumance and SI Agriculture ) are plotted for Armed Conflict Location and Event Dataset (ACLED) pixels related to pastoralist conflicts in SaSu during these two periods (see Fig. 5 ). The comparative analysis reveals a significant increase in conflicts during 2011–2020 compared to 2001–2010 [Figure 5 (a)]. The increase in conflicts is particularly pronounced in areas with high transhumance suitability (0.4 < SI Transhumance ≤ 0.8), exceeding 7650 occurrence pixels for 0.4 < SI Transhumance ≤ 0.8 areas during 2011–2020, compared to less than 300 occurrence pixels for the same suitability classes for 2001–2010. The comparison of two decades shows the spread of conflict from 0.4 < SI Transhumance ≤ 0.8 classes to 0 < SI Transhumance ≤ 1 classes. Using the SI Agriculture for the same pastoralist-related conflict pixels, the frequency and probability distribution of conflict occurrences are graphically represented for SI Agriculture pixels associated with agricultural suitability in SaSu during these specified time periods [see Fig. 5 (b)]. The comparative analysis reveals a notable escalation of conflicts for low and medium quality agricultural lands during 2011–2020 compared to the previous decade [0 ≤ SI Agriculture < 0.6 classes, Fig. 5 (b)]. Figure 5 (b) also shows that although pastoralist-related conflicts increased in all SI Agriculture classes, the spatial increase was towards lower-quality agricultural lands. 3. Discussion 3.1. Suitable lands for transhumance The transhumance suitability analysis across the SaSu region provides a comprehensive understanding of land suitability for pastoral mobility, emphasizing the spatial variability in resources critical for transhumance. As mentioned by previous studies, the dominance of areas classified as medium and good suitability (67.3% of the region) underscores the potential for extensive transhumance activities 7,10,12,45–47 . This spatial distribution indicates that SaSu supports pastoralist activities, ensuring sufficient biomass availability, water resources, and market accessibility in these regions. However, the presence of unsuitable or poorly suitable areas (32.3%; Fig. 1) highlights the natural limitations imposed by environmental constraints like sparse vegetation or limited water access 8 . For the first time, the spatial analysis introduced in this study provides the required tool to identify suitability gradient and consequently the transhumance corridors in SaSu, revealing key pathways and reliable dry-season locations 34 . Our findings can inform resource allocation and planning, particularly in regions prone to resource scarcity during seasonal transitions. The validation of the SI Transhumance map with both qualitative and quantitative methods confirms its reliability and strengthens its application for sustainable pastoral planning 8 . The agreement between expert-provided transhumance corridors and the SI Transhumance map’s predictions emphasizes our result’s alignment with observations despite the inherent uncertainty of traditional knowledge systems. Similarly, using information on recorded conflicts for quantitative validation highlights the SI Transhumance map’s robustness in reflecting potential pastoralist presence across SaSu 21 . One of the key contributions of this study is its predictive capability in data-poor regions where validation points or qualitative corridor data are scarce. The analysis fills these gaps by leveraging EO data and provides a roadmap for targeted field validation and policy intervention 8,46 . The delineation of international transhumance corridors is particularly relevant for cross-border resource management and conflict mitigation, offering a valuable tool for regional collaboration 3,10,46 . The study’s novel results advance understanding of transhumance dynamics by integrating multi-source EO data and generating a validated, scalable suitability index map. The ability to capture seasonal and interannual variations enriches the application of this approach for dynamic resource management, especially in regions experiencing climate variability and socio-political pressures 48–51 . In this study, the distance from roads is a critical factor in assessing the suitability of transhumance. The historical link between pastoralists and the landscape predates modern road infrastructure. Initially, pastoralist routes were essential routes long before formal roads were established. The advent of vehicles led to the transformation of some pastoralist paths into formal roads. While pastoralists now use some of these roads and vehicles for transhumance 8 , they may still venture into more distant and challenging terrain as long as they can sustain themselves and their livestock. The current study assumes that transhumance corridors are based on the long-term suitability index without a temporal simulation of pastoralist movements as agents. This means this study is a supply-based assessment for transhumance. However, there is a need for demand-based assessment, which depends on the social factors of pastoralism and the demography of their livestock 52 . The results of this study suggest that knowing the behavior of pastoralists as agents in a grid-based simulation, or agent-based simulation, as a demand-based assessment, can be the next step and will provide insight to account for the behavior of all three pastoralist groups 26,53,54 . Such supply-demand-based simulations 26 could capture the emergent behaviors of transhumance spatio-temporal patterns by incorporating initial locations, movement rules, and landscape data using EO datasets. However, conducting such simulations is beyond the scope of this study, which focuses on landscape suitability patterns derived from remotely sensed data rather than dynamic, emergent transhumance behaviors. 3.2. Suitable lands for agriculture The assessment of agricultural suitability in SaSu provides valuable insights into the limitations and potential of cropland within the region. The results indicate that 96.4% of the croplands identified through the EO-based agreement layer exhibit at least minimal suitability for agriculture 55 . This finding aligns closely with the total extent of cropland delineated in the cropland agreement layer for 2020, supporting the robustness of the EO-based analysis 55 . The spatial variability in suitability, particularly the dominance of low water availability as a limiting factor, highlights the critical role of water resources in determining agricultural potential across SaSu 56–58 . The identification of 3.6% of croplands as unsuitable for agriculture underscores the need for targeted interventions in regions with poor soil quality 59 . These findings emphasize the importance of addressing soil health, soil conservation, and water resource availability to enhance agricultural productivity 60 . The results reveal that the cropland agreement layer, particularly for agreement levels > 0, represents the maximum potential land for pastoralist-farmer conflict. This observation underscores the interplay between agricultural and pastoral land use and the need for integrated land management policies that minimize conflict 8,20,43 . Identifying areas where land is marginally suitable for agriculture but critical for pastoral mobility can inform conflict prevention strategies and resource-sharing frameworks 8 . SaSu exhibits a wide range of spatial and temporal variation, ranging from the more humid regions to the most extreme dry areas on Earth. The agricultural suitability method used in this study to assess agricultural lands was developed primarily on FAO suitability recommendations 35 , regardless of the biome classification of the landscape. It is important to note that this method, by focusing on precipitation, may not comprehensively cover agricultural lands in oasis-like arid areas since they depend on groundwater resources. In this agricultural suitability methodology, the primary water constraint is based on precipitation, and the role of irrigation is not explicitly addressed. While there are a few agricultural suitability studies in Africa, none cover much of the SaSu, resulting in a lack of a tested and verified method for this vast region. Conversely, a similar method has been tested globally 38 . Our analysis, neglecting some of this global analysis’s constraints, resulted in more expansive suitable areas for agriculture. Similar studies have demonstrated the effectiveness of the implemented method globally 38 or at the national scale 30,35,61 . These studies cover different types of agriculture, such as rainfed and irrigated, large and small scale, and with high dependence on surface water or groundwater. Previous research has validated the efficacy of the method in other arid or semi-arid areas for assessing agricultural land suitability, demonstrating consistency with ecological footprint accounting for bio capcity 62 , satellite imagery observations 30 , and water footprint accounting 61 . There are limited studies to validate the agricultural cropland agreement product used in our study for SaSu. However, global assessments and evaluations for California, Costa Rica, and Belgium indicate an accuracy of 85% for the EO 10 m resolution validation data for agriculture 63 (one of the six datasets in the cropland agreement layer). Therefore, combining and using all six datasets for croplands provides the most comprehensive cropland extent. The results show that the main limiting factors for sub-Saharan agriculture are water, the soil CEC, and soil organic matter 56,57,59,60,64 . Climate change and drought cause the decline in freshwater availability and soil quality in Africa 57,65,66 . On the other hand, agriculture methods can degrade or improve the quality of soil for agriculture 59,60 . We used the most recent data for African soil properties with a spatial resolution of 30 m. The EO data show the soil properties for two soil layers, 0–20 cm, and 20–50 cm. We also used the mean of these two layers to create the SI Agriculture layer for that soil property. However, we recognize that not only are the EO-based soil layers uncertain, but the fuzzy membership function for them may also be spatially variable or variable based on crop and growing season. Smaller-scale studies and more tailored membership functions for this area are needed to address these concerns. This study advances the understanding of agricultural suitability in SaSu by integrating EO data with a multi-source cropland agreement layer. The spatial analysis of limiting factors offers valuable insights into the region’s agricultural potential and provides a foundation for targeted interventions to enhance productivity 22,25,53 . By delineating suitability across agreement levels, the analysis contributes to the broader discourse on land-use conflicts and sustainable resource management in sub-Saharan Africa 8,20,43 . 3.3. Conflict zones The conflict data provided by ACLED 21 provide locations of incidents, which are reliable information for the validation of transhumance corridors. At the same time, there is no assessment of the spatial accuracy of this dataset. Some records of this datasets are not spatially accurate because they are located in unlikely places when visually inspecting the map. On the other hand, it is important to acknowledge the potential presence of non-pastoral, non-farmer actors in conflicts as a second actor. This potential is particularly relevant given the diversity of conflicts in Africa, some of which are driven by political, religious, or terrorist motives. At the same time, there may be unrecorded conflicts. To address this limitation and calculate the probability of pastoralist-farmer conflict, we introduced the conditional probability of agricultural land into our analysis, knowing that the precision of the data is not ideal for SaSu. Specifically, the probability values assigned to the suitable pixels for transhumance and agriculture are based on fuzzy functions. The derived fuzzy probability functions are based on the validation of the SI Transhumance and SI Agriculture using ACLED and EO data, which are affected by their accuracy. The agricultural suitability analysis confirms that SI Agriculture > 0 for 95% of the croplands based on the cropland agreement layer. However, the cropland agreement layer has uncertainties and inaccuracies (e.g., for the border between Sudan and South Sudan or eastern Mali). Also, we accept all the ACLED records as accurate without knowing their accuracy. The results show high probability conflict areas with a low-density of recorded pastoralist-related conflict data. There may be several reasons for this: The conflict database is not complete for these regions due to missing records. They may be low in settlement or high in land productivity, and while there is potential for conflict due to low numbers of livestock and people or high food availability, there is no reason for conflict. These areas may be future conflict-prone regions that are susceptible to conflict in the future. Other triggers may be necessary for conflict to occur, mainly controlled by social factors. In this situation, competition for resources is a prerequisite, but not a sufficient reason for conflict. Therefore, there are fewer de facto conflicts than the possible hot spot conflict regions based on environmental factors. The pastoralist and other actors did find mechanisms to prevent conflict. One potential source of discrepancy is that agricultural suitability is determined without considering the seasonal pattern of agriculture. This seasonal pattern for agriculture can be modeled using process-based agricultural simulations that require information on land preparation, cropping season and schedule, crop types, and irrigation schedule. As the main limiting factor, the water availability for surface water 57 and groundwater 67 should be determined first. In contrast, the transhumance suitability in this study is based on monthly interannual data. Consequently, conflicts could be inferred as likely in such cells, even though the actual occurrence depends on seasonal variations not explicitly captured in our analysis. In this study, SI Transhumance and SI Agriculture are long-term static variables for each pixel, calculated based on long-term data without considering temporal dynamics such as climate change or drought impacts. The increase in conflict and wider spread towards lower and higher SI Transhumance areas and lower SI Agriculture areas suggests several possibilities: Transhumance may have shifted to lower quality land, either because of diminishing resources in high SI Transhumance areas or because of an increase in the number of pastoralists and their livestock, leading to resource use in low SI Transhumance areas and consequently more conflicts. Conflicts in high SI Transhumance areas can be attributed to conflicts between pastoralists and farmers, as these areas are suitable for both groups. Conversely, low SI Agriculture areas, which are mainly suitable for transhumance, show evidence of agricultural presence based on EO data. The EO data for all six agreement classes show the presence of croplands for SI Agriculture = 0 (Fig. 3)—these lands are suitable for seasonal agriculture, possibly used by farmers or pastoralists themselves. From 2001 to 2010, conflicts were less frequent but concentrated in lands more suitable for transhumance [see Fig. 5(a)]. In these areas, there are sufficient resources to support livestock, and transhumance itself acts as a preventative mechanism unless there is competition from an immobile competitor (permanent farming or human settlement) or if the number of grazing livestock exceeds the carrying capacity of the land. As competition persisted on high-quality lands, it subsequently spread to lower quality lands suitable for transhumance. Similarly, the expansion of agricultural land by farmers may have occurred. We should consider that the second actor in pastoralist-related conflicts may be another pastoralist, farmers, settlers (in the built-up areas), or a military group. Suppose that these conflict pixels are located in cropland cultivated by farmers (and not by pastoralists). In this case, therefore, the conflict may be caused by the change in cropping timing, cropping pattern, the land use change from grassland to cropland by farmers, or the expansion of croplands by farmers in such a way they occupy the transhumance corridors (e.g., the number of farmers increases, and the area of cropland increases) which is already beyond the agricultural carrying capacity (SI = 0) for these lands. In this case, the conflict will increase with the same number of pastoralists and livestock (e.g., within the transhumance carrying capacity) or with an increasing number of pastoralists and livestock beyond the carrying capacity. If pastoralists cultivate these croplands they may have turned to farmers (or seasonal farmers). In that case, they are half-nomads with a fixed settlement. We guess that the increase in conflict numbers for this class is probably due to an increase in the pastoralist population or their livestock population beyond the seasonal carrying capacity for the fixed settlement. The exceeding of the carrying capacity by the population caused an inevitable environmental collapse (land degradation) and conflict in the half-nomad society (through the occurrence of conflict between the half-nomads using the land as cropland and the other pastoralists/half-nomads). However, there is another likely scenario. It is possible that the amount of biomass has decreased due to the long-term effects of climate-related phenomena such as drought or climate change. In this case, even if the number of pastoralists or farmers remains constant and within the carrying capacity limits of a region, the amount of resources will decrease. This study can not address this probable scenario since the short period of 2001–2020 is insufficient for a climate-related assessment. 3.4. Limitations of the study This study identifies potential transhumance corridors in SaSu and delineates seasonal suitability of transhumance based on water and biomass availability. However, several limitations should be acknowledged. The determination of specific paths chosen by pastoralists involves inherent uncertainties. Path selection is a dynamic and complex decision-making process influenced by numerous factors beyond environmental considerations such as water and biomass availability. These factors include livestock type, market destinations, seasonal weather conditions, herd size, travel distances, landmarks, tribal agreements and traditions, and interactions with local communities or government policies. As such, the corridors identified in this study reflect environmental suitability rather than capturing the full spectrum of pastoralist behaviors. The study assumes that monthly time-step data on water and vegetation cover, in combination with road proximity, provide sufficient spatiotemporal resolution for corridor identification. While this approach balances computational feasibility and ecological representation, using data with finer temporal resolution might capture greater variability in spatial water availability, potentially resulting in more scattered or fragmented corridor outputs. Road accessibility is modeled using Euclidean distance with a linear diminishing factor extending up to 30 km. While this parameterization provides a useful approximation, it does not account for non-linear diminishing effects, which may better reflect pastoralists’ preferences based on factors such as road quality, terrain, or livestock-specific needs. Moreover, the maximum distance threshold of 30 km might vary depending on specific pastoralist decisions or the demographics of their herds. Validation of the identified corridors is another key challenge. The lack of observed GPS-tracked paths for pastoralists across the vast expanse of SaSu limits the ability to quantitatively assess the model’s accuracy. Current validation mainly relies on expert opinion and qualitative maps published in previous studies, which, while valuable, are inherently subjective. Acquiring more detailed and accurate tracking data is critical for refining the model and ensuring more robust validation in future research. 4. Methods In Fig. 6 , the methodology employed in this research is elucidated through a flowchart that overviews the steps undertaken in the study. The subsequent sections of this paper will offer a detailed explanation of the method elements outlined in Fig. 6 . 4.1. Case Study The study area encompasses the semi-arid Sahelian and Sudanian (SaSu) zone 68 , based on terrestrial ecoregions of the world 69 (See also https://www.oneearth.org/navigator/ ), between the Saharan desert and the humid Guinean zone, extending from the Atlantic coast to the Red Sea coast (Fig. 6 ). SaSu is a vast region covering over 533.8 million hectares, characterized by diverse geography, including deserts, savannas, mountains, and coastal plains. This region represents a unique bioclimatic zone, hosting some of the last remaining intact wilderness areas globally and as a high-priority focus for wildlife conservation 70 . Encompassing 17 countries, SaSu is marked by Nigeria as the most populous, followed by Ethiopia. Water resources within SaSu exhibit significant heterogeneity, posing challenges regarding availability and distribution. Annual precipitation in the region varies from less than 50 mm close to the Sahara to exceeding 800 mm in certain parts beside Central Africa. Surface water and groundwater resources are limited, with varying accessibility across regions. SaSu is home to rural communities that are heavily reliant on the land for sustenance, particularly pastoralists. Human prosperity in this area is intricately linked to vegetation resources, given that approximately 80% of the rapidly growing population depends on traditional livelihood strategies, such as subsistence agriculture and livestock production via transhumance 19 . 4.2. Land suitability This step aims to identify areas suitable for transhumance 8 and agriculture 35 . The agricultural suitability assessment is based on the previous studies, incorporating fuzzy membership functions aligned with FAO recommendations 35 , 37 , 38 . Both assessments utilize more recent EO data sources 30 , 61 , 71 . Overlaying various geospatial information layers enables the determination of the Suitability Index (SI) map. Table 1 and Table 2 present the layers employed in this process. The SI values standardize the data into a scaled range from 0 (not suitable) to 1 (very suitable), with excluded areas marked by SI = 0. Fuzzy membership functions for transhumance and agriculture suitability are illustrated in Figure S1 and Figure S2, respectively. The final SI Transhumance index is the mean of six SI layers for transhumance suitability 8 . Each input variable’s SI map is calculated using the respective EO layer (Table 1 ) and its fuzzy membership function (Figrue S1). For agricultural suitability, the final SI Agriculture index is computed following Liebig’s law of minimum for all 11 calculated SI layers 35 (Table 2 ) and their fuzzy membership functions (Figure S2). Finally, the SI Transhumance and SI Agriculture layers are classified into suitability classes 35 , as unsuitable (SI = 0), very poor (0 < SI ≤ 0.2), poor (0.2 < SI ≤ 0.4), medium (0.4 < SI ≤ 0.6), good (0.6 < SI ≤ 0.8), and very good (0.8 < SI ≤ 1). 4.3. Geospatial data for suitability analysis The data processing for this part is performed and all figures were generated in the Google Earth Engine (GEE) platform. To this goal, geospatial data and layers are aggregated from diverse sources, comprising 24 datasets. These encompass information on the water availability index, vegetation cover, land cover, climate, soil properties, topography, excluded areas, and validation datasets. Comprehensive details regarding the utilized data, along with their descriptions and sources, are available in Table 1 and Table 2 . 4.3.1. Geospatial data for transhumance suitability The assessment of surface water availability employed the BioHydroGenerator v.4.3 tool 72 , utilizing the Small Water Bodies product from Copernicus Global Land Service (CGLS) as a primary input. To evaluate surface water accessibility, BioHydroGenerator establishes a 30 km buffer ring around identified cells, prioritizing them using a decreasing Gaussian weighting function as the water accessibility index (WAI), ranging from 0 to 1 as follows. $$\:WAI\left(d\right)=\left(1-{F}_{BG}\right)\times\:\text{exp}\left(-\frac{{d}^{2}}{2\times\:{\sigma\:}^{2}}\right)+{F}_{BG}$$ 1 where, \(\:d\) represents the distance to the water point in kilometers, while \(\:\sigma\:\) denotes a parameter of the Gaussian function adjusted to extend 1% beyond 30 km ( \(\:\sigma\:\:=\:\frac{30}{\sqrt{2\times\:Ln\left(100\right)}}\) ). Additionally, \(\:{F}_{BG}\) corresponds to the background WAI, which varies based on aridity zones 52 , showcasing a gradual transition from 0% in hyper-arid areas to 100% in humid regions. The WAI scale spans from 0 to 1, where 0 signifies no access to water, and 1 indicates the presence of a permanent water point 52 . WAI layers are generated monthly throughout the 2001–2020 period. Additionally, the Vegetation Cover Index (VCI) and Road Accessibility Index (RAI) were calculated. Based on MODIS acquisitions, the VCI layers were derived from Total Vegetation Cover Products from GEOGLAM-RAPP, encompassing monthly vegetation coverage fractions, including green and dry vegetation. The road layer was obtained from the GADM database ( https://www.gadm.org/ ), and the urban land cover class was extracted from global land cover data 73 . The Euclidean distance to roads and urban areas was considered, incorporating the road/urban area accessibility index (RAI) as follows: $$\:RAI\left(d\right)=\left\{\begin{array}{cc}\frac{30-d}{30}&\:d\le\:30\\\:0&\:d>30\end{array}\right.$$ 2 where 30 km refers to the maximum distance from the roads. Inner urban areas were treated as unsuitable for transhumance corridors, indicated by zero suitability as excluded areas. Urban and road layers were considered static for the modeling period (2001 to 2020). For both water access and road access layers, the 30 km buffer distance is based on a maximum of two days of walking to access a water resource 12 . The final SI Transhumance index (0–1) is the mean of WAI, VCI, RAI layers. 4.3.2. Geospatial data for agriculture suitability Eight variables are included in the calculation of SI Agriculture : precipitation, available soil water content, soil texture, coarse soil fragments, soil pH, soil cation exchange capacity (CEC), soil organic carbon (OC), and slope. We also excluded two land uses: road/built areas and water bodies. We did not exclude protected areas and forests because the EO data show that farmers violate these protections in several places (Table 2 ). Given the data constraints for Africa, several simplifications are introduced in comparison to the original reference 35 . The first simplification pertains to variables related to soil suitability, including soil salinity, sodicity, base saturation, and the percentage of gypsum, which are omitted for the overall soil suitability, adopting a more optimistic approach. Another simplification mentioned in the original reference 35 relates to irrigated croplands. This method primarily focuses on long-term agriculture full-year cropping, such as agriculture without the need for irrigation, reliant on precipitation as the primary water source, neglecting the complexities of irrigation where water delivery mechanisms are unknown. This study does not consider climate change impacts, including shifts in growing seasons and precipitation timing. Freezing point effects on agricultural suitability are also simplified, adopting a more optimistic stance toward the climate component of agriculture. In the SaSu region, where precipitation is already limited, the effects of temperature on the growing season are not considered, contributing to a more optimistic perspective on the climate component of agriculture. This study assumes that African farmers are dynamic agents capable of adapting to changing environments. Consequently, inter-annual variations in precipitation timing over the years are not considered, and calculations for agriculture suitability rely on long-term average data. In contrast, transhumance is inherently seasonal, leading to the calculation of two water availability and vegetation cover datasets for transhumance suitability on a monthly time scale. However, each month’s average is used based on acquired data from 2001 to 2020. Temporal aspect is crucial in agriculture to account for changes in land use over time. Based on the cropland agreement layer, one of the six datasets (GFSAD1km) represents the extent of cropland for 2010. Some areas designated as suitable for agriculture may have changed to other land uses. Another dataset (ESA landcover), representing land use for 2020, has a much smaller area than the 2010 layer. We assume that suitable agricultural land, in a static form without temporal variations due to drought or climate change, represents land that is cultivated permanently (irrigated) or seasonally (rainfed) during a given period. To account for this, we utilized the cropland agreement layer with agreement level ≥ 1. However, it is essential to acknowledge that this combination lacks a long-term accuracy assessment. Table 1 List of RS/GIS data used for the transhumance suitability analysis of SaSu region. GEE refers to Google Earth Engine Layer No. Name Source (Reference) or GEE layer name Main 6 suitability layers 1 Monthly water availability BioHydroGenerator 72 2 Monthly vegetation coverage fraction GEOGLAM-RAPP, based on MODIS acquisitions 3 Africa tree cover https://doi.org/10.5281/zenodo.7764460 74,75 4 Roads https://www.gadm.org/ 5 Urban areas https://doi.org/10.5281/zenodo.5571936 63 ee.ImageCollection("ESA/WorldCover/v100") 6 MODIS layer provided by NASA NASA 76 ee.ImageCollection("MODIS/006/MOD17A3HGF") Auxialary layers to produce the maps 7 Administrative borders Large Scale International Boundaries ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") 8 Inland water bodies https://doi.org/10.5281/zenodo.5571936 63 ee.ImageCollection("ESA/WorldCover/v100") 9 Croplands agreement https://doi.org/10.5281/zenodo.7244124 44,55 Validation layers for transhumance 10 Pastoralist’s conflicts An Armed Conflict Location and Event Dataset (ACLED) 21 https://doi.org/10.1177/0022343310378914 11 Pastoralist’s transhumance corridors 37 Maps in the published reports and resources Table 2 List of RS/GIS data used for the suitability analysis of SaSu for agriculture. GEE refers to Google Earth Engine Layer No. Name Source (Reference) or GEE layer name Climate 12 Mean annual precipitation (mm) ee.ImageCollection("IDAHO_EPSCOR/TERRACLIMATE") Soil properties 13 pH (H 2 O) ee.Image("OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02") 14 Cation Exchange Capacity (cmol c /kg) ee.Image("ISDASOIL/Africa/v1/cation_exchange_capacity") 15 Organic carbon (%) ee.Image("ISDASOIL/Africa/v1/carbon_organic") 16 Coarse fragments (%) ee.Image("ISDASOIL/Africa/v1/stone_content") 17 Texture ee.Image("ISDASOIL/Africa/v1/texture_class") 18 Available Water Content (mm/m) ee.Image("OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01") Topography 19 Slope (%) NASA SRTM Digital Elevation 30 m ee.Image("USGS/SRTMGL1_003") Excluded areas https://doi.org/10.5281/zenodo.5571936 63 20 Inland water bodies https://doi.org/10.5281/zenodo.5571936 63 ee.ImageCollection("ESA/WorldCover/v100") 21 Protected areas https://www.protectedplanet.net 70 ee.FeatureCollection("WCMC/WDPA/current/polygons") 5 Urban areas https://doi.org/10.5281/zenodo.5571936 63 ee.ImageCollection("ESA/WorldCover/v100") Validation layers for agriculture 9 Croplands agreement https://doi.org/10.5281/zenodo.7244124 44,55 Table 3 Suitability index for nutrient availability, rooting conditions, and workability variables as a lookup function of USDA’s soil textures. Values are based on FAO’s recommendation 35 , 37 Texture Nutrient availability Rooting conditions Workability Clay (heavy) 1 0.91 0.82 Silty clay 1 1 1 Silty clay loam 1 1 1 Clay loam 1 1 1 Silt 1 1 1 Silt loam 1 1 1 Sandy clay 1 1 1 Loam 1 1 1 Sandy clay loam 1 0.99 1 Sandy loam 0.9 0.99 1 Loamy sand 0.69 0.99 1 Sand 0.35 0.98 1 4.3.3. Geospatial validation data Multiple datasets involve validating the transhumance corridors, pastoralist-related conflicts, and agricultural lands. Validation data for transhumance corridors comprises two main groups. The first group covers the northwest of Africa, providing insights into migration corridors observed by pastoralists, as documented in one reference 77 . The second group results from an exhaustive literature review, analyzing more than 50 records and extracting 36 maps depicting transhumance corridors. While these maps serve as valuable qualitative sources, they inherently carry spatial uncertainty regarding the precise location of the corridors. For pastoralist-related conflicts and the SI Transhumance validation, the Armed Conflict Location and Event Data Project 21 (ACLED) is the primary validation dataset, covering the period from 2001 to 2020 ( https://www.acleddata.com/ ). This dataset includes conflict locations where one or both actors are identified as pastoralists. This dataset filters conflicts involving at least one pastoralist actor, resulting in a total of 4372 conflict points from 2001 to 2020. In the validation process, the point layer is converted to a raster layer, with each conflict point represented by a circle with a 500 m radius circle (500 m is chosen based on processing limitations and the number of pixels limit for SaSu in GEE). The raster analysis performed with a 500 m × 500 m pixel size results in 8940 pixels representing conflicts. Validation data for agricultural lands relies on the cropland agreement dataset 44 . This dataset offers geospatial information on the agreement-disagreement classifications of six open-access high-resolution cropland maps derived from remote sensing. Each point in this dataset is ranked from 6 to 0, where 6 signifies agreement among all six sources on the cropland, and 0 indicates unanimous agreement on the non-cropland characteristic of the land. 4.4. The logic behind the transhumance corridors and possible conflict zones The pastoralist lifestyle is based on the livestock and feeding the animals. Therefore, the main bottlenecks for pastoralists are environmental factors 8 . Among the six considered factors (water, vegetation cover, green cover, forest cover, roads, and urban area) 8 , water is the primary environmental limiting factor, followed by vegetation cover (as the two supply factors in the animal life cycle), and access to the markets (vicinity of population settlements like roads and cities) is the primary social limiting factor (as a demand factor for the animal life cycle). Therefore, in the mind of pastoralists, a transhumance corridor compromises supply and demand for their livestock while providing water and feed on the path. Pastoralist knows their transhumance paths by heart and uses multi-generational experience. Due to the institutional shaping of this knowledge, they persistently use the same transhumance path every year 78 . This compromise can be modeled by inter-annual averaging water access and vegetation coverage on monthly time steps and road/urban access as long-term static layers 8 . Also, the suitability gradient over months shows the path from high SI Transhumance to low SI Transhumance , similar to flow routing based on topography, resulting in transhumance corridors. The transhumance corridor network is quantified using spatial analysis, based on the assumption that pastoralists move from less suitable to more suitable areas in their vicinity due to seasonal changes. The SI Transhumance map, developed using the Google Earth Engine (GEE), is subjected to further analysis in the ArcGIS environment. Although this method shows the hotspots suitable for wet and dry seasons and the optimal path to connect these suitable hotspots, this method doesn’t consider the definite start, end and distance of transhumance for a group of pastoralists. Therefore, a transhumance corridor in this study shows the passage and not the itinerary. Therefore, classifying the pastoralists based on their behavior for moving is not possible with the current study. There are eight cases for farmer-herder conflict 20 ; (1) Resource competition: Land, grazing, and water rivalry (68%); (2) Cattle or crop damage: Trampling, rustling, blocking, pollution (24%); (3) Intergroup animosity: Social, religious, and cultural hostilities; (4) Migration: Climate-induced, historical patterns, ethnic tensions; (5) Land tenure insecurity: Ambiguous laws, contested rights, insecurity; (6) State weakness: Weak policies, lack of action, government support; (7) Nonstate armed groups: Affiliation, attacks, self-defense; and (8) Historical patterns: Colonial evictions, policy impact, grazing conflicts. Researchers 20 identified resource competition as the primary cause of conflict, especially for arable land, grazing land, and water access. Therefore, a potential conflict zone is already occupied by farmers as croplands (either irrigated or rainfed) located on the transhumance corridor. This conditional probability function can estimate the probability of the pastoralist-farmer conflict: $$\:{Fuzzy}_{Conflict}\left(x\right)={Fuzzy\:SI}_{Agriculture}\left(x\right|x\in\:Agreed\:Cropland)\times\:{Fuzzy\:SI}_{Transhumance}(x)$$ 3 where \(\:{Fuzzy}_{Conflict}\left(x\right)\) is the fuzzy probability of conflict for the location \(\:x\) , \(\:{Fuzzy\:SI}_{Agriculture}\left(x\right|x\in\:Agreed\:Cropland)\) is the fuzzy probability function of conflict based on SI Agriculture for location \(\:x\) if it is a cropland pixel based on the cropland agreement layer, and \(\:{Fuzzy\:SI}_{Transhumance}\left(x\right)\) is the fuzzy probability function of conflict based on SI Transhumance . This analysis uses two derived fuzzy functions [Figure S3(c, d)] to fuzzify the SI Transhumance and SI Agriculture maps. The first fuzzy function for SI Transhumance is based on the distribution of conflict points over 2001–2020. The second fuzzy function is based on the SI Agriculture and the distribution of conflict points in the suitability classes for the same period. Based on the two fuzzy functions, the SI Transhumance and SI Agriculture are mapped to their fuzzy versions with conflict scores from 0 to 1. The overall fuzzy map for conflicts is obtained by multiplying the two fuzzy maps. It is worth mentioning that this method calculated the probability by considering environmental factors affecting SI Transhumance and SI Agriculture . The complete conflict probability estimate is beyond the competition for water and food resources since it is a function of combined environmental, social, demographic, behavioral, and economic drivers contingent on multiple short-term and long-term social institutions like politics and history. At the same time, conflict is just one of the resulting encounters between farmers and herders in an “aggressive” manner 20 . There are “passive” and “constructive” encounters 20 , which are beyond this study’s scope. Declarations Acknowledgment This work was supported by the Pioneer Center for Landscape Research in Sustainable Agricultural Futures (Land-CRAFT), DNRF grant number P2, Aarhus, Denmark, and Institut National de la Recherche Scientifique (INRS), Quebec, Canada. The authors would like to thank Professor André St-Hilaire for providing the opportunity to conduct this research as a collaborative research internship for Mostafa Khorsandi. We thank Gülnur Dogan (Ph.D.), Center Manager of Land-CRAFT for her kind support of this project. Furthermore, we thank INRS for granting Mostafa Khorsandi the International Mobility and Short Stays Outside Quebec Program (PMICSE) in 2023. Data availability The datasets used in this study are publicly available on the Google Earth Engine data catalog. Code availability The codes to create agricultural suitability layer are available here: https://code.earthengine.google.com/191a60c504884dee8649e3b024a51043 Also, the codes to create the transhumance suitability layer and transhumance corridors are available here: https://code.earthengine.google.com/17ac3801375e6061603e43f2dbb64ca8 Competing interests The authors declare no competing interests. Author contributions MK, K.B.-B., and JR conceived and designed the study. MK and EF preprocessed the data, and MK performed the modeling and analyses. MK wrote the first draft with support from JR, while other co-authors (E.F., A.S., and K.B.-B.) contributed to improving the manuscript. References Schlolz, F. & Schlee, G. in International Encyclopedia of the Social & Behavioral Sciences (Second Edition) (ed James D. Wright) 838-843 (Elsevier, 2015). Lees, S. H. & Bates, D. G. The Origins of Specialized Nomadic Pastoralism: A Systemic Model. American Antiquity 39 , 187-193 (1974). https://doi.org/10.2307/279581 Stenning, D. J. Transhumance, migratory drift, migration; patterns of pastoral Fulani nomadism. The Journal of the Royal Anthropological Institute of Great Britain and Ireland 87 , 57-73 (1957). Jacquemot, P. Pastoralism in Africa A way of life in danger? (2023). Meadows, D. H., Randers, J. & Meadows, D. A Synopsis: Limits to Growth: The 30-Year Update. Estados Unidos: Chelsea Green Publishing Company 381 (2004). Meadows, D. H., Goldsmith, E. I. & Meadows, P. The limits to growth. (Earth Island Limited London, 1972). Scheper, C. et al. The role of agro-ecological factors and transboundary transhumance in shaping the genetic diversity in four indigenous cattle populations of Benin. Journal of Animal Breeding and Genetics 137 , 622-640 (2020). https://doi.org/https://doi.org/10.1111/jbg.12495 Schwarz, M. et al. Assessing the Environmental Suitability for Transhumance in Support of Conflict Prevention in the Sahel. Remote Sensing 14 , 1109 (2022). Duporge, I. et al. A satellite perspective on the movement decisions of African elephants in relation to nomadic pastoralists. Remote Sensing in Ecology and Conservation (2022). Houessou, S. O. et al. The role of cross-border transhumance in influencing resident herders’ cattle husbandry practices and use of genetic resources. Animal 14 , 2378-2386 (2020). https://doi.org/https://doi.org/10.1017/S1751731120001378 Turner, M. D. & Schlecht, E. Livestock mobility in sub-Saharan Africa: A critical review. Pastoralism 9 , 13 (2019). https://doi.org/10.1186/s13570-019-0150-z Motta, P. et al. Cattle transhumance and agropastoral nomadic herding practices in Central Cameroon. BMC veterinary research 14 , 1-12 (2018). Luizza, M. Transhumant Pastoralism in Central Africa: Emerging Impacts on Conservation and Security. Unpublished report. US Fish & Wildlife Service, Washington, DC, USA (2017). N.A. Pastoralist and Farmer-Herder Conflicts in the Sahel. Climate Diplomacy (2023). Jobbins, M. & McDonnell, A. Pastoralism and conflict: Tools for prevention and response in the Sudano-Sahel. Search for Common Ground , 1-110 (2021). Brottem, L. Growing Complexity of Farmer-Herder Conflict in West and Central Africa. (2021). Krätli, S. & Toulmin, C. Farmer-herder conflict in sub-Saharan Africa? , (International Institute for Environment and Development (IIED) London, UK, 2020). Loveridge, A. J. et al. Bells, bomas and beefsteak: complex patterns of human-predator conflict at the wildlife-agropastoral interface in Zimbabwe. PeerJ 5 , e2898 (2017). Zhongming, Z., Linong, L., Xiaona, Y., Wangqiang, Z. & Wei, L. Livelihood security: Climate change, migration and conflict in the Sahel. (2011). Adams, E. A., Thill, A., Kuusaana, E. D. & Mittag, A. Farmer–herder conflicts in sub-Saharan Africa: drivers, impacts, and resolution and peacebuilding strategies. Environmental Research Letters 18 , 123001 (2023). https://doi.org/10.1088/1748-9326/ad0702 Raleigh, C., Linke, r., Hegre, H. & Karlsen, J. Introducing ACLED: An Armed Conflict Location and Event Dataset. Journal of Peace Research 47 , 651-660 (2010). https://doi.org/10.1177/0022343310378914 Terfa, B. K. & Suryabhagavan, K. V. Rangeland suitability evaluation for livestock production using remote sensing and GIS techniques in dire district, southern Ethiopia. Global Journal of Science Frontier Research: H Environment & Earth Science 15 (2015). Sensing, N. U. R. Rangeland suitability for livestock grazing and economic implications in irepodun area of Osun state Nigeria using remote sensing and GIS techniques. (2016). Strohbach, B. J. Making more of vegetation classification results: a livestock farming Suitability Index as tool for land-use planning in Namibia. Phytocoenologia 48 , 7-22 (2018). Balew, A. et al. Identification of Suitable Land for Livestock Production Using GIS-Based Multicriteria Decision Analysis and Remote Sensing in the Bale Lowlands, Ethiopia. International Journal of Ecology 2022 , 9585552 (2022). https://doi.org/10.1155/2022/9585552 Gebeyehu, A. K., Sonneveld, B. G. J. S. & Snelder, D. J. Identifying Hotspots of Overgrazing in Pastoral Areas: Livestock Mobility and Fodder Supply–Demand Balances in Nyangatom, Lower Omo Valley, Ethiopia. Sustainability 13 , 3260 (2021). Farazmand, A., Arzani, H., Javadi, S. & Sanadgol, A. Determining the factors affecting rangeland suitability for livestock and wildlife grazing. Applied Ecology & Environmental research 17 , 317-329 (2019). Karlson, M. & Ostwald, M. Remote sensing of vegetation in the Sudano-Sahelian zone: A literature review from 1975 to 2014. Journal of Arid Environments 124 , 257-269 (2016). Jahnke, H. E. & Jahnke, H. E. Livestock production systems and livestock development in tropical Africa . Vol. 35 (Kieler Wissenschaftsverlag Vauk Kiel, 1982). Khorsandi, M., Homayouni, S. & van Oel, P. The edge of the petri dish for a nation: Water resources carrying capacity assessment for Iran. Science of The Total Environment 817 , 153038 (2022). https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.153038 Running, S. W. A regional look at HANPP: human consumption is increasing, NPP is not. Environmental Research Letters 9 , 111003 (2014). Sircely, J., Conant, R. T. & Boone, R. B. Simulating Rangeland Ecosystems with G-Range: Model Description and Evaluation at Global and Site Scales. Rangeland Ecology & Management 72 , 846-857 (2019). https://doi.org/https://doi.org/10.1016/j.rama.2019.03.002 Rahimi, J. et al. Beyond livestock carrying capacity in the Sahelian and Sudanian zones of West Africa. Scientific reports 11 , 1-15 (2021). Maman Moutari, E. & Giraut, F. Le corridor de transhumance au Sahel : un archétype de territoire multisitué ? L’Espace géographique 42 , 306-323 (2013). https://doi.org/10.3917/eg.424.0306 Mesgaran, M. B., Madani, K., Hashemi, H. & Azadi, P. Iran’s land suitability for agriculture. Scientific reports 7 , 7670 (2017). Baskaran, V., Madasamy, M., Kumar, S. P. & Sahana, S. V. Modeling the land suitability for agricultural utility in a semi-arid region of Tirunelveli district, South India using multi-criteria and geospatial approach. Modeling Earth Systems and Environment (2023). https://doi.org/10.1007/s40808-023-01706-5 Fischer, G. et al. Global Agro-ecological Zones (GAEZ v4)-Model Documentation. (2021). Zabel, F., Putzenlechner, B. & Mauser, W. Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9 , e107522 (2014). https://doi.org/10.1371/journal.pone.0107522 Van Ittersum, M. K. et al. Can sub-Saharan Africa feed itself? Proceedings of the National Academy of Sciences 113 , 14964-14969 (2016). Mberu, B. U. & Ezeh, A. C. The population factor and economic growth and development in Sub-Saharan African countries. African Population Studies 31 (2017). Ezeh, A., Kissling, F. & Singer, P. Why sub-Saharan Africa might exceed its projected population size by 2100. The Lancet 396 , 1131-1133 (2020). Rahimi, J., Smerald, A., Moutahir, H., Khorsandi, M. & Butterbach-Bahl, K. The potential consequences of grain-trade disruption on food security in the Middle East and North Africa region. Frontiers in Nutrition 10 (2023). https://doi.org/https://doi.org/10.3389/fnut.2023.1239548 Nassef, M., Eba, B., Islam, K., Djohy, G. & Flintan, F. E. Causes of farmer–herder conflicts in Africa: A systematic scoping review. (2023). Tubiello, F. N. et al. A new cropland area database by country circa 2020. Earth Syst. Sci. Data 15 , 4997-5015 (2023). https://doi.org/10.5194/essd-15-4997-2023 Mbih, R. A., Ndzeidze, S. K., Wanyama, D. & Mbuh, M. J. Challenges of transhumance in Northwest Cameroon. SN Social Sciences 2 , 208 (2022). https://doi.org/10.1007/s43545-022-00515-4 Ouedraogo, A. S. et al. Cross border transhumance involvement in ticks and tick-borne pathogens dissemination and first evidence of Anaplasma centrale in Burkina Faso. Ticks and Tick-borne Diseases 12 , 101781 (2021). https://doi.org/https://doi.org/10.1016/j.ttbdis.2021.101781 Léonard, U. B., MOUSSA. The Dynamics and Impacts of Transhumance and Neo-Pastoralism on Biodiversity, Local Communities and Security: Congo Basin. (2021). Tugjamba, N., Walkerden, G. & Miller, F. Adapting nomadic pastoralism to climate change. Climatic Change 176 , 28 (2023). https://doi.org/10.1007/s10584-023-03509-0 Opitz-Stapleton, S. Transboundary climate risks to african dryland livestock economies. (2023). Wardropper, C. B. et al. Improving rangeland climate services for ranchers and pastoralists with social science. Current Opinion in Environmental Sustainability 52 , 82-91 (2021). https://doi.org/https://doi.org/10.1016/j.cosust.2021.07.001 Simpson, N. P. et al. A framework for complex climate change risk assessment. One Earth 4 , 489-501 (2021). https://doi.org/https://doi.org/10.1016/j.oneear.2021.03.005 Rahimi, J. et al. A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africa’s drylands. Nature Food 3 , 523-531 (2022). https://doi.org/10.1038/s43016-022-00543-6 Abdi, A., Seaquist, J., Tenenbaum, D., Eklundh, L. & Ardö, J. The supply and demand of net primary production in the Sahel. Environmental Research Letters 9 , 094003 (2014). Bhaumik, S. K. & Nugent, J. B. Analysis of Food Demand in Peru: Implications for Food–Feed Competition. Review of Development Economics 3 , 242-257 (1999). https://doi.org/https://doi.org/10.1111/1467-9361.00065 Tubiello, F. N. et al. (Zenodo, 2022). Cobbing, J. & Hiller, B. Waking a sleeping giant: Realizing the potential of groundwater in Sub-Saharan Africa. World Development 122 , 597-613 (2019). Faramarzi, M. et al. Modeling impacts of climate change on freshwater availability in Africa. Journal of Hydrology 480 , 85-101 (2013). https://doi.org/https://doi.org/10.1016/j.jhydrol.2012.12.016 MacDonald, A. M., Bonsor, H. C., Dochartaigh, B. É. Ó. & Taylor, R. G. Quantitative maps of groundwater resources in Africa. Environmental Research Letters 7 , 024009 (2012). Dossouhoui, G. I. A., Yemadje, P. L., Diogo, R. V. C., Balarabe, O. & Tittonell, P. “Sedentarisation” of transhumant pastoralists results in privatization of resources and soil fertility decline in West Africa's cotton belt. Frontiers in Sustainable Food Systems 7 (2023). https://doi.org/10.3389/fsufs.2023.1120315 Assogba, G. G. C., Berre, D., Adam, M. & Descheemaeker, K. Can low-input agriculture in semi-arid Burkina Faso feed its soil, livestock and people? European Journal of Agronomy 151 , 126983 (2023). https://doi.org/https://doi.org/10.1016/j.eja.2023.126983 Khorsandi, M., Omidi, T. & van Oel, P. Water-related limits to growth for agriculture in Iran. Heliyon 9 , e16132 (2023). https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e16132 Khorsandi, M., Bateni, M. M. & Van Oel, P. A mathematical meta-model for assessing the self-sufficient water resources carrying capacity across different spatial scales in Iran. Heliyon (2023). Karra, K. et al. in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. 4704-4707 (IEEE). Springer, A., Lopez, T., Owor, M., Frappart, F. & Stieglitz, T. The Role of Space-Based Observations for Groundwater Resource Monitoring over Africa. Surveys in Geophysics 44 , 123-172 (2023). https://doi.org/10.1007/s10712-022-09759-4 Cuthbert, M. O. et al. Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa. Nature 572 , 230-234 (2019). https://doi.org/10.1038/s41586-019-1441-7 Bonsor, H., Shamsudduha, M., Marchant, B., Macdonald, A. M. & Taylor, R. Seasonal and decadal groundwater changes in African sedimentary aquifers estimated using GRACE products and LSMs. Remote Sensing 10 , 904 (2018). Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H. P., Lin, H. X. & Bierkens, M. F. P. GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geosci. Model Dev. 17 , 275-300 (2024). https://doi.org/10.5194/gmd-17-275-2024 Souverijns, N. et al. Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. Remote Sensing 12 , 3817 (2020). Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51 , 933-938 (2001). https://doi.org/10.1641/0006-3568(2001)051[0933:Teotwa]2.0.Co;2 UNEP-WCMC & IUCN. (ed UNEP-WCMC and IUCN) (Cambridge, UK: UNEP-WCMC and IUCN, 2024). Hengl, T. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Scientific Reports 11 , 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y Fillol, E. Biohydrogenerator User Guide. (2018). Zanaga, D. et al. ESA WorldCover 10 m 2020 v100. (2021). https://doi.org/10.5281/ZENODO.5571936 Reiner, F. et al. More than one quarter of Africa’s tree cover is found outside areas previously classified as forest. Nature Communications 14 , 2258 (2023). https://doi.org/10.1038/s41467-023-37880-4 Reiner, F. et al. (Zenodo, 2023). Running, S. W. & Zhao, M. Daily GPP and annual NPP (MOD17A2/A3) products NASA Earth Observing System MODIS land algorithm. MOD17 User’s Guide 2015 (2015). Higazi, A. & Abubakar Ali, S. Pastoralism and Security in West Africa and the Sahel: Towards Peaceful Coexistence. (2018). Xiao, N., Cai, S., Moritz, M., Garabed, R. & Pomeroy, L. W. Spatial and Temporal Characteristics of Pastoral Mobility in the Far North Region, Cameroon: Data Analysis and Modeling. PLOS ONE 10 , e0131697 (2015). https://doi.org/10.1371/journal.pone.0131697 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5860400","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":419276124,"identity":"c378a481-b2fb-4d5f-ac7e-6e17b1137d8c","order_by":0,"name":"Mostafa Khorsandi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYNACGwYGCQkIk7GNvYGBmYB6xgaGNGQtPAdI1dIgkYBfi257+/MHPxLsGCRnNx/7+KPmjmyf5BuzxwU1DPL8Ddi1mJ05Y9jYk5DMIC1zLHk2z7Fnxm3SOebGM44xGM44gEPLjRzGBt4fzAxyEjnGzAxshxOBWsykeRsYEhhwakl/2PgnoR6shfHHP6AWyTMQLfI4tSQYNvMkHGaQBmph4G0DapHggWgxwKUF6JfZMgnHeSRnpCUz8/YdNm7jSSuTnnFMwnAjLi3H2x98fJNQLSdxI/kw449vh2Xntx/eJl1QYyMvh0MLDPCgC0jgVz8KRsEoGAWjAC8AAE9sV/C+Ha5VAAAAAElFTkSuQmCC","orcid":"","institution":"Institut national de la recherche scientifique (INRS)","correspondingAuthor":true,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Khorsandi","suffix":""},{"id":419276125,"identity":"b97656a0-19d2-4cf2-a1b8-383fc9c18d91","order_by":1,"name":"Erwann Fillol","email":"","orcid":"","institution":"Action Against Hunger (AAH)","correspondingAuthor":false,"prefix":"","firstName":"Erwann","middleName":"","lastName":"Fillol","suffix":""},{"id":419276126,"identity":"1aad0dd4-f954-4fa4-8cc8-a2713f2e93e4","order_by":2,"name":"Andrew Smerald","email":"","orcid":"https://orcid.org/0000-0003-2026-273X","institution":"Institute of Meteorology and Climate Research Atmospheric Environmental Research (IMK-IFU) Karlsruhe Institute of Technology (KIT)","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Smerald","suffix":""},{"id":419276127,"identity":"e31c6fee-33fa-47e0-8d60-299e0e18b4f8","order_by":3,"name":"Klaus Butterbach-Bahl","email":"","orcid":"","institution":"Institute for Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Klaus","middleName":"","lastName":"Butterbach-Bahl","suffix":""},{"id":419276128,"identity":"07de83fc-01ab-4689-a54b-2519519ea1a8","order_by":4,"name":"Jaber Rahimi","email":"","orcid":"https://orcid.org/0000-0002-2754-2358","institution":"Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT)","correspondingAuthor":false,"prefix":"","firstName":"Jaber","middleName":"","lastName":"Rahimi","suffix":""}],"badges":[],"createdAt":"2025-01-19 16:05:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5860400/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5860400/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77331200,"identity":"62890c9f-3eb1-4e27-bbd2-2542b3d8cce5","added_by":"auto","created_at":"2025-02-27 13:23:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":227450,"visible":true,"origin":"","legend":"\u003cp\u003eTranshumance suitability (SI\u003csub\u003eTranshumance\u003c/sub\u003e) map for SaSu based on water availability, vegetation cover, tree cover, biomass availability, and road accessibility. The area for each suitability class is given in parentheses in the unit of million hectares. This method shows the suitability gradient, with very good, good, and medium classes identifying reliable dry-season lands, while very poor and poor classes indicate potential wet-season destinations for pastoralists.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/7efdeaef2c745e37c904b194.png"},{"id":77331222,"identity":"96fb4337-5a6e-48a8-95a6-be50b75d8615","added_by":"auto","created_at":"2025-02-27 13:23:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":475294,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Transhumance corridors derived from digitization of maps available in the literature for SaSu in different colors based on 37 sources (see Table S1 in the Supplementary Material) and (b) transhumance corridors (in Blue color) derived from transhumance suitability analysis in this study for SaSu region. The analysis is performed using the spatial analysis toolbox in ArcGIS.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/b38083fbbecb27cb9c7a5ab0.png"},{"id":77331579,"identity":"3b7b083c-7014-4a50-a783-51bfd74922fc","added_by":"auto","created_at":"2025-02-27 13:31:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":764639,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum potential cropland area in the study region\u003csup\u003e44\u003c/sup\u003e, differentiated by agricultural suitability analysis using 11 limiting variables such as precipitation and soil properties (for details see Section 4.2). Suitability classes are calculated for each level of cropland agreement (agreement level 1 to 6).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/3d24c738e6730c810c826aa1.png"},{"id":77331193,"identity":"fee3cba5-5a9d-4c42-9687-dd84cb7f3227","added_by":"auto","created_at":"2025-02-27 13:23:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":324108,"visible":true,"origin":"","legend":"\u003cp\u003eSaSu pastoralist-farmer conflict map based on the fuzzy probability (Fuzzy\u003csub\u003eConflict\u003c/sub\u003e) of the SI\u003csub\u003eTranshumance\u003c/sub\u003e (Figure 1) and SI\u003csub\u003eAgriculture\u003c/sub\u003e (Figure 3) maps and classification into 0 to 1 conflict probability classes. Conflict zones are marked by dotted boxes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/aa396a7026d0fca7ec6d3ff7.png"},{"id":77331186,"identity":"382fb58e-6848-41f2-8163-09d7639a5785","added_by":"auto","created_at":"2025-02-27 13:23:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123818,"visible":true,"origin":"","legend":"\u003cp\u003eOccurrence of ACLED conflict pixels related to pastoralists in SaSu for two time periods (2001-2010 and 2011-2020). (a) shows the number and percentage of ACLED conflict pixels within each suitability class used for transhumance suitability (SI\u003csub\u003eTranshumance\u003c/sub\u003e) analysis, and (b) shows the number and percentage of ACLED conflict pixels within each suitability class used for agriculture suitability analysis (SI\u003csub\u003eAgriculture\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/b4d878601280ebb56ca211bd.png"},{"id":77331573,"identity":"137e1f12-cd2d-4473-8bc1-1062f457fd6a","added_by":"auto","created_at":"2025-02-27 13:31:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":614542,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and overview of the methodology employed for modeling the transhumance and agriculture suitability analysis, deriving transhumance corridors and conflict hotspot zones.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/e152a92782dcb33d27d1b12e.png"},{"id":92732328,"identity":"43d340db-97b1-4c54-98d1-170c0bfd8ab0","added_by":"auto","created_at":"2025-10-03 16:04:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3736346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/5fdebcd6-439e-4d08-ae98-5c3500d2c3cb.pdf"},{"id":77331191,"identity":"c5e21759-6f61-49b6-addf-aafac39fa9b6","added_by":"auto","created_at":"2025-02-27 13:23:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3739109,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5860400/v1/f27beb07dae84847946c0cc2.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Prospects for pastoralist-farmer conflict in Africa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHistorically, nomadic pastoralism has been a key adaptation to the variable environment\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and has persisted despite the changes brought about by the Industrial and Green Revolutions, where new approaches of resource use have emerged\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Despite these changes, pastoralism remains a sustainable way of harmonizing human activities with nature\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Africa, which benefits from the presence of pastoralists for food production while preserving its ecosystems\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, faces challenges as human impact on ecosystems increases the struggle for limited resources, endangering pastoralists\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and occasionally leading to conflict\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In Nigeria alone, between 2016 and 2018, a total of 3,727 deaths and numerous casualties were reported due to conflicts between pastoralists and farmers\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The number of casualties is much higher in the SaSu region\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA fundamental step in the sustainability analysis of transhumance in Africa is to assess the suitability of land for this way of life. While localized studies of the feasibility of transhumance exist, such as in Southern Ethiopia\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, Osun State of Nigeria\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, Namibia\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, Republic of Chad, and the Central African Republic\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, the widespread occurrence of transhumance and the lack of tracking data make Earth Observation (EO)-based methods the most viable method for large-scale studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. While factors influencing transhumance have been discussed in several sources\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, EO studies for assessing the suitability of land for transhumance in the SaSu are lacking.\u003c/p\u003e \u003cp\u003eKey determinants include water availability, vegetation cover, and their seasonal variations\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Net primary production (NPP), derived from satellite imagery, is also a valuable tool for studying land productivity, with applications ranging from agricultural production assessment\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e to food production\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and rangeland capacity assessment\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In SaSu, satellite-based NPP products can help to determine biomass availability for transhumance migration routes around livestock grazing areas\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Despite the knowledge of the main environmental drivers of transhumance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, most studies have been limited to a single year or a specific region, thereby potentially overlooking interannual or long-term changes in transhumance activities and pathways.\u003c/p\u003e \u003cp\u003eAt the same time, EO data and global-scale data sources can be used to detect and assess agricultural land used either by farmers or partially by pastoralists\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, agricultural suitability assessments have been applied in arid and semi-arid regions of the world to identify land for farming\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Agriculture suitability criteria have also been well developed by international agencies\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, no previous study shows the suitability of agricultural land for SaSu.\u003c/p\u003e \u003cp\u003eAfter pastoralists, farmers are the other major users of natural resources in SaSu for food production. Africa has a high demand for food due to its growing population, which is projected to increase from 9% of the world\u0026rsquo;s population in 1950 to 35% in 2100\u003csup\u003e39,40\u003c/sup\u003e. The SaSu is no exception, and the population will increase 2.5-fold or higher between 2010 and 2050 period\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, most of the food demand will be imported into the SaSu countries (e.g., for cereals alone, the current self-sufficiency ratio of 0.79\u0026ndash;0.87 will decrease to 0.33\u0026ndash;0.48 in 2050)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. There are two methods for meeting the high internal demand for food in SaSu: (1) increasing the cropland area and (2) closing the yield gap between current and potential yields on existing cropland\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This increase in cropland area is likely to increase the number of conflicts between pastoralists and farmers in SaSu.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that research on the causes of conflict between farmer-pastoralist conflict is scarce\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The review found a direct link between the conflict and land or natural resources with an increasing trend; however, there was little quantitative evidence to support this\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. On the other hand, some studies emphasize that governance or social factors cause conflict in addition to resource scarcity/competition\u003csup\u003e43\u003c/sup\u003e. In this regard, more and deeper research on the relationship between resource availability and pastoralist-farmer conflict is emphasized\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study we developed a geospatial tool to assess the suitability index for transhumance (SI\u003csub\u003eTranshumance\u003c/sub\u003e) in SaSu, which allows to narrow down suitable areas into high-resolution transhumance corridors, and the assessment of the suitability of land for cropping systems using the suitability index for agriculture (SI\u003csub\u003eAgricluture\u003c/sub\u003e). Based on this approach we identify conflict-prone areas due to competition of pastoralists and farmers using the suitability index (SI) analysis and EO-based data. Thus, our analysis provides an improved understanding of transhumance, in order to facilitate transhumance planning, and identify potential conflict risk areas on the basis of a wide range of EO data.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eThe suitability of transhumance and agriculture was assessed by overlaying standardized geospatial layers, categorized into six classes: unsuitable (SI\u0026thinsp;=\u0026thinsp;0), very poor (0\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.2), poor (0.2\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.4), medium (0.4\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.6), good (0.6\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.8), and very good (0.8\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;1). These classifications aim to quantify the potential scale of agriculture-pastoralist conflict.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Land suitability for transhumance\u003c/h2\u003e \u003cp\u003eThe suitability of land for transhumance was evaluated using an overlapping fuzzy membership function technique of EO data, incorporating six layers, including water, vegetation cover, tree cover, roads, urban areas, and MODIS product. Two main limiting factors for transhumance\u0026mdash;water accessibility and vegetated land cover as livestock feed sources\u0026mdash;are incorporated in our assessment using monthly layers to capture seasonal variations (Section \u0026lrm;4.3.1). Results indicate that approximately 21% of the study area (112.1\u0026nbsp;million hectares) is classified as good or high-quality for transhumance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eValidation of the SI\u003csub\u003eTranshumance\u003c/sub\u003e map was conducted qualitatively by comparing systematically digitized transhumance corridors [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a)], largely derived from pastoralist inputs during collaborative meetings (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for sources). While these insights are valuable, their precision is limited due to (1) annual variability in transhumance routes, (2) challenges of representing a sub-continental region within a limited engagement period, and (3) the imprecision of verbal communication. In addition, our study highlights a more spatially detailed SI\u003csub\u003eTranshumance\u003c/sub\u003e map compared to available knowledge, enhancing the accuracy of corridor delineation across the SaSu region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Land suitability for agriculture\u003c/h2\u003e \u003cp\u003eAgricultural suitability was derived from eight EO layers using variables such as precipitation, soil parameters, and slope. The analysis leveraged a previous study combining six global cropland datasets to identify areas of cropland agreement\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This consensus-based layer estimates 185.5\u0026nbsp;million hectares of cropland in SaSu, while non-croplands with high agreement total 348.3\u0026nbsp;million hectares (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The agricultural suitability analysis focused on agreed croplands (agreement levels 1\u0026ndash;6; number of sources identifying the area as cropland).\u003c/p\u003e \u003cp\u003eResults reveal that 3.6% (6.7\u0026nbsp;million hectares) of agreed croplands are unsuitable for arable farming due to low soil quality (low CEC and OC) or water scarcity. The remaining croplands were classified from very poor to very good suitability, with water availability being the primary limiting factor. Approximately 27.2% (50.4\u0026nbsp;million hectares) of cropland with low suitability (unsuitable or poor) falls in areas with low agreement (levels\u0026thinsp;\u0026le;\u0026thinsp;3), indicating seasonal rainfed practices with low yields.\u003c/p\u003e \u003cp\u003eNevertheless, 96.4% (178.8\u0026nbsp;million hectares) of EO-based croplands exhibit a minimum suitability (SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026gt; 0), consistent with the 2020 cropland extent (185.5\u0026nbsp;million hectares). This agreement confirms the suitability of these lands for agriculture and identifies the maximum potential overlap for pastoralist-farmer conflict.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Potential zones for pastoralist-farmer conflict\u003c/h2\u003e \u003cp\u003eThe SaSu region is divided into three transhumance suitability zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): areas with SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026lt; 0.25 and SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026gt; 0.8 show rare transhumance activity (\u0026lt;\u0026thinsp;5%), while the primary zone for transhumance (95%) lies within 0.25\u0026thinsp;\u0026le;\u0026thinsp;SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026le; 0.8 [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure S3(c)]. Using cropland agreement classes, 69.3% of conflicts occur in areas agreed upon as cropland, 30.7% occur in non-cropland areas, and 3.4% occur in areas with high cropland agreement. This suggests that the 30.7% in non-cropland areas likely represent pastoralist-pastoralist conflicts, unrecorded croplands, or limitations in the input data [Figures S3(b) and S4].\u003c/p\u003e \u003cp\u003eFor pastoralist-related conflicts, zones with SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026lt; 0.05 and SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026gt; 0.5 show rare occurrences, while the primary conflict zone (0.05\u0026thinsp;\u0026le;\u0026thinsp;SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026le; 0.5) is within croplands [Figure S3(d)]. This range aligns with areas of frequent interactions between transhumance and agriculture, leading to higher conflict intensity. Figure S3 demonstrates a high correspondence between the SI\u003csub\u003eTranshumance\u003c/sub\u003e map and the reported pastoralist conflict locations in Figure S4(a). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates hotspots for pastoralist-farmer conflicts, derived from the combination of Fuzzy SI\u003csub\u003eTranshumance\u003c/sub\u003e and Fuzzy SI\u003csub\u003eAgriculture\u003c/sub\u003e, highlighting areas most prone to tensions. The validation points for recorded conflicts confirm the conflict hot spots in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. By placing adjacent conflict points in a zone, there are 19 hot spot conflict zones in SaSu.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure S4 examines conflict pixels reported between 2001 and 2020 and classifies them into six agreement classes based on cropland agreement. The results show that 30.7% of reported conflicts occur in non-cropland areas, suggesting pastoralist-pastoralist conflicts or low accuracy of the agreement layer in discriminating cropland from non-cropland for these regions. Notably, most conflicts, constituting 69.3%, are found in agreement classes 1\u0026ndash;6. These agreement classes indicate how many sources identified a pixel as cropland. If a pixel is classified as cropland, it may represent either rainfed or irrigated land. This makes it challenging to determine the second actor in the conflict\u0026mdash;whether it is a farmer or another pastoralist\u0026mdash;beyond the first pastoralist involved. However, the fact that SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026gt; 0 for these lands and that they are reported as croplands, suggests the strong possibility of pastoralist-farmer conflict. Again, by classifying the conflict pixels based on SI\u003csub\u003eAgriculture\u003c/sub\u003e, almost none of the conflicts are in high quality areas for agriculture [SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026gt; 0.8, Figure S3(b)]. On the other hand, almost 15% of the conflicts are located in areas unsuitable for permanent cultivation which are likely to be rainfed and not cropped all year round [Figure S3(b)]. Three of the six agricultural suitability classes (very poor, poor, and medium) serve as common pool resources for both farmers and pastoralists, where competing interests over environmental resource use play a key role in conflicts (Figure S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Pastoralists\u0026rsquo; conflict over time\u003c/h2\u003e \u003cp\u003eTo understand the temporal dynamics of pastoralist conflicts, the study period is divided into two sub-periods: 2001\u0026ndash;2010 and 2011\u0026ndash;2020. The frequency of conflict occurrence and the probability distribution based on the suitability index (both SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e) are plotted for Armed Conflict Location and Event Dataset (ACLED) pixels related to pastoralist conflicts in SaSu during these two periods (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe comparative analysis reveals a significant increase in conflicts during 2011\u0026ndash;2020 compared to 2001\u0026ndash;2010 [Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a)]. The increase in conflicts is particularly pronounced in areas with high transhumance suitability (0.4\u0026thinsp;\u0026lt;\u0026thinsp;SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026le; 0.8), exceeding 7650 occurrence pixels for 0.4\u0026thinsp;\u0026lt;\u0026thinsp;SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026le; 0.8 areas during 2011\u0026ndash;2020, compared to less than 300 occurrence pixels for the same suitability classes for 2001\u0026ndash;2010. The comparison of two decades shows the spread of conflict from 0.4\u0026thinsp;\u0026lt;\u0026thinsp;SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026le; 0.8 classes to 0\u0026thinsp;\u0026lt;\u0026thinsp;SI\u003csub\u003eTranshumance\u003c/sub\u003e \u0026le; 1 classes.\u003c/p\u003e \u003cp\u003eUsing the SI\u003csub\u003eAgriculture\u003c/sub\u003e for the same pastoralist-related conflict pixels, the frequency and probability distribution of conflict occurrences are graphically represented for SI\u003csub\u003eAgriculture\u003c/sub\u003e pixels associated with agricultural suitability in SaSu during these specified time periods [see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b)]. The comparative analysis reveals a notable escalation of conflicts for low and medium quality agricultural lands during 2011\u0026ndash;2020 compared to the previous decade [0\u0026thinsp;\u0026le;\u0026thinsp;SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026lt; 0.6 classes, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b)]. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b) also shows that although pastoralist-related conflicts increased in all SI\u003csub\u003eAgriculture\u003c/sub\u003e classes, the spatial increase was towards lower-quality agricultural lands.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.1. Suitable lands for transhumance\u003c/h2\u003e\n \u003cp\u003eThe transhumance suitability analysis across the SaSu region provides a comprehensive understanding of land suitability for pastoral mobility, emphasizing the spatial variability in resources critical for transhumance. As mentioned by previous studies, the dominance of areas classified as medium and good suitability (67.3% of the region) underscores the potential for extensive transhumance activities\u003csup\u003e7,10,12,45–47\u003c/sup\u003e. This spatial distribution indicates that SaSu supports pastoralist activities, ensuring sufficient biomass availability, water resources, and market accessibility in these regions. However, the presence of unsuitable or poorly suitable areas (32.3%; Fig.\u0026nbsp;1) highlights the natural limitations imposed by environmental constraints like sparse vegetation or limited water access\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eFor the first time, the spatial analysis introduced in this study provides the required tool to identify suitability gradient and consequently the transhumance corridors in SaSu, revealing key pathways and reliable dry-season locations\u003csup\u003e34\u003c/sup\u003e. Our findings can inform resource allocation and planning, particularly in regions prone to resource scarcity during seasonal transitions.\u003c/p\u003e\n \u003cp\u003eThe validation of the SI\u003csub\u003eTranshumance\u003c/sub\u003e map with both qualitative and quantitative methods confirms its reliability and strengthens its application for sustainable pastoral planning\u003csup\u003e8\u003c/sup\u003e. The agreement between expert-provided transhumance corridors and the SI\u003csub\u003eTranshumance\u003c/sub\u003e map’s predictions emphasizes our result’s alignment with observations despite the inherent uncertainty of traditional knowledge systems. Similarly, using information on recorded conflicts for quantitative validation highlights the SI\u003csub\u003eTranshumance\u003c/sub\u003e map’s robustness in reflecting potential pastoralist presence across SaSu\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eOne of the key contributions of this study is its predictive capability in data-poor regions where validation points or qualitative corridor data are scarce. The analysis fills these gaps by leveraging EO data and provides a roadmap for targeted field validation and policy intervention\u003csup\u003e8,46\u003c/sup\u003e. The delineation of international transhumance corridors is particularly relevant for cross-border resource management and conflict mitigation, offering a valuable tool for regional collaboration\u003csup\u003e3,10,46\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe study’s novel results advance understanding of transhumance dynamics by integrating multi-source EO data and generating a validated, scalable suitability index map. The ability to capture seasonal and interannual variations enriches the application of this approach for dynamic resource management, especially in regions experiencing climate variability and socio-political pressures\u003csup\u003e48–51\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eIn this study, the distance from roads is a critical factor in assessing the suitability of transhumance. The historical link between pastoralists and the landscape predates modern road infrastructure. Initially, pastoralist routes were essential routes long before formal roads were established. The advent of vehicles led to the transformation of some pastoralist paths into formal roads. While pastoralists now use some of these roads and vehicles for transhumance\u003csup\u003e8\u003c/sup\u003e, they may still venture into more distant and challenging terrain as long as they can sustain themselves and their livestock.\u003c/p\u003e\n \u003cp\u003eThe current study assumes that transhumance corridors are based on the long-term suitability index without a temporal simulation of pastoralist movements as agents. This means this study is a supply-based assessment for transhumance. However, there is a need for demand-based assessment, which depends on the social factors of pastoralism and the demography of their livestock\u003csup\u003e52\u003c/sup\u003e. The results of this study suggest that knowing the behavior of pastoralists as agents in a grid-based simulation, or agent-based simulation, as a demand-based assessment, can be the next step and will provide insight to account for the behavior of all three pastoralist groups\u003csup\u003e26,53,54\u003c/sup\u003e. Such supply-demand-based simulations\u003csup\u003e26\u003c/sup\u003e could capture the emergent behaviors of transhumance spatio-temporal patterns by incorporating initial locations, movement rules, and landscape data using EO datasets. However, conducting such simulations is beyond the scope of this study, which focuses on landscape suitability patterns derived from remotely sensed data rather than dynamic, emergent transhumance behaviors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.2. Suitable lands for agriculture\u003c/h2\u003e\n \u003cp\u003eThe assessment of agricultural suitability in SaSu provides valuable insights into the limitations and potential of cropland within the region. The results indicate that 96.4% of the croplands identified through the EO-based agreement layer exhibit at least minimal suitability for agriculture\u003csup\u003e55\u003c/sup\u003e. This finding aligns closely with the total extent of cropland delineated in the cropland agreement layer for 2020, supporting the robustness of the EO-based analysis\u003csup\u003e55\u003c/sup\u003e. The spatial variability in suitability, particularly the dominance of low water availability as a limiting factor, highlights the critical role of water resources in determining agricultural potential across SaSu\u003csup\u003e56–58\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe identification of 3.6% of croplands as unsuitable for agriculture underscores the need for targeted interventions in regions with poor soil quality\u003csup\u003e59\u003c/sup\u003e. These findings emphasize the importance of addressing soil health, soil conservation, and water resource availability to enhance agricultural productivity\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe results reveal that the cropland agreement layer, particularly for agreement levels \u0026gt; 0, represents the maximum potential land for pastoralist-farmer conflict. This observation underscores the interplay between agricultural and pastoral land use and the need for integrated land management policies that minimize conflict\u003csup\u003e8,20,43\u003c/sup\u003e. Identifying areas where land is marginally suitable for agriculture but critical for pastoral mobility can inform conflict prevention strategies and resource-sharing frameworks\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eSaSu exhibits a wide range of spatial and temporal variation, ranging from the more humid regions to the most extreme dry areas on Earth. The agricultural suitability method used in this study to assess agricultural lands was developed primarily on FAO suitability recommendations\u003csup\u003e35\u003c/sup\u003e, regardless of the biome classification of the landscape. It is important to note that this method, by focusing on precipitation, may not comprehensively cover agricultural lands in oasis-like arid areas since they depend on groundwater resources. In this agricultural suitability methodology, the primary water constraint is based on precipitation, and the role of irrigation is not explicitly addressed. While there are a few agricultural suitability studies in Africa, none cover much of the SaSu, resulting in a lack of a tested and verified method for this vast region. Conversely, a similar method has been tested globally\u003csup\u003e38\u003c/sup\u003e. Our analysis, neglecting some of this global analysis’s constraints, resulted in more expansive suitable areas for agriculture. Similar studies have demonstrated the effectiveness of the implemented method globally\u003csup\u003e38\u003c/sup\u003e or at the national scale\u003csup\u003e30,35,61\u003c/sup\u003e. These studies cover different types of agriculture, such as rainfed and irrigated, large and small scale, and with high dependence on surface water or groundwater. Previous research has validated the efficacy of the method in other arid or semi-arid areas for assessing agricultural land suitability, demonstrating consistency with ecological footprint accounting for bio capcity\u003csup\u003e62\u003c/sup\u003e, satellite imagery observations\u003csup\u003e30\u003c/sup\u003e, and water footprint accounting\u003csup\u003e61\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThere are limited studies to validate the agricultural cropland agreement product used in our study for SaSu. However, global assessments and evaluations for California, Costa Rica, and Belgium indicate an accuracy of 85% for the EO 10 m resolution validation data for agriculture\u003csup\u003e63\u003c/sup\u003e (one of the six datasets in the cropland agreement layer). Therefore, combining and using all six datasets for croplands provides the most comprehensive cropland extent.\u003c/p\u003e\n \u003cp\u003eThe results show that the main limiting factors for sub-Saharan agriculture are water, the soil CEC, and soil organic matter\u003csup\u003e56,57,59,60,64\u003c/sup\u003e. Climate change and drought cause the decline in freshwater availability and soil quality in Africa\u003csup\u003e57,65,66\u003c/sup\u003e. On the other hand, agriculture methods can degrade or improve the quality of soil for agriculture\u003csup\u003e59,60\u003c/sup\u003e. We used the most recent data for African soil properties with a spatial resolution of 30 m. The EO data show the soil properties for two soil layers, 0–20 cm, and 20–50 cm. We also used the mean of these two layers to create the SI\u003csub\u003eAgriculture\u003c/sub\u003e layer for that soil property. However, we recognize that not only are the EO-based soil layers uncertain, but the fuzzy membership function for them may also be spatially variable or variable based on crop and growing season. Smaller-scale studies and more tailored membership functions for this area are needed to address these concerns.\u003c/p\u003e\n \u003cp\u003eThis study advances the understanding of agricultural suitability in SaSu by integrating EO data with a multi-source cropland agreement layer. The spatial analysis of limiting factors offers valuable insights into the region’s agricultural potential and provides a foundation for targeted interventions to enhance productivity\u003csup\u003e22,25,53\u003c/sup\u003e. By delineating suitability across agreement levels, the analysis contributes to the broader discourse on land-use conflicts and sustainable resource management in sub-Saharan Africa\u003csup\u003e8,20,43\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.3. Conflict zones\u003c/h2\u003e\n \u003cp\u003eThe conflict data provided by ACLED\u003csup\u003e21\u003c/sup\u003e provide locations of incidents, which are reliable information for the validation of transhumance corridors. At the same time, there is no assessment of the spatial accuracy of this dataset. Some records of this datasets are not spatially accurate because they are located in unlikely places when visually inspecting the map. On the other hand, it is important to acknowledge the potential presence of non-pastoral, non-farmer actors in conflicts as a second actor. This potential is particularly relevant given the diversity of conflicts in Africa, some of which are driven by political, religious, or terrorist motives. At the same time, there may be unrecorded conflicts.\u003c/p\u003e\n \u003cp\u003eTo address this limitation and calculate the probability of pastoralist-farmer conflict, we introduced the conditional probability of agricultural land into our analysis, knowing that the precision of the data is not ideal for SaSu. Specifically, the probability values assigned to the suitable pixels for transhumance and agriculture are based on fuzzy functions. The derived fuzzy probability functions are based on the validation of the SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e using ACLED and EO data, which are affected by their accuracy. The agricultural suitability analysis confirms that SI\u003csub\u003eAgriculture\u003c/sub\u003e \u0026gt; 0 for 95% of the croplands based on the cropland agreement layer. However, the cropland agreement layer has uncertainties and inaccuracies (e.g., for the border between Sudan and South Sudan or eastern Mali). Also, we accept all the ACLED records as accurate without knowing their accuracy.\u003c/p\u003e\n \u003cp\u003eThe results show high probability conflict areas with a low-density of recorded pastoralist-related conflict data. There may be several reasons for this:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe conflict database is not complete for these regions due to missing records.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThey may be low in settlement or high in land productivity, and while there is potential for conflict due to low numbers of livestock and people or high food availability, there is no reason for conflict.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThese areas may be future conflict-prone regions that are susceptible to conflict in the future.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eOther triggers may be necessary for conflict to occur, mainly controlled by social factors. In this situation, competition for resources is a prerequisite, but not a sufficient reason for conflict. Therefore, there are fewer de facto conflicts than the possible hot spot conflict regions based on environmental factors.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe pastoralist and other actors did find mechanisms to prevent conflict.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eOne potential source of discrepancy is that agricultural suitability is determined without considering the seasonal pattern of agriculture. This seasonal pattern for agriculture can be modeled using process-based agricultural simulations that require information on land preparation, cropping season and schedule, crop types, and irrigation schedule. As the main limiting factor, the water availability for surface water\u003csup\u003e57\u003c/sup\u003e and groundwater\u003csup\u003e67\u003c/sup\u003e should be determined first. In contrast, the transhumance suitability in this study is based on monthly interannual data. Consequently, conflicts could be inferred as likely in such cells, even though the actual occurrence depends on seasonal variations not explicitly captured in our analysis.\u003c/p\u003e\n \u003cp\u003eIn this study, SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e are long-term static variables for each pixel, calculated based on long-term data without considering temporal dynamics such as climate change or drought impacts. The increase in conflict and wider spread towards lower and higher SI\u003csub\u003eTranshumance\u003c/sub\u003e areas and lower SI\u003csub\u003eAgriculture\u003c/sub\u003e areas suggests several possibilities:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eTranshumance may have shifted to lower quality land, either because of diminishing resources in high SI\u003csub\u003eTranshumance\u003c/sub\u003e areas or because of an increase in the number of pastoralists and their livestock, leading to resource use in low SI\u003csub\u003eTranshumance\u003c/sub\u003e areas and consequently more conflicts.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cul\u003e\n \u003cli\u003eConflicts\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ein high SI\u003csub\u003eTranshumance\u003c/sub\u003e areas can be attributed to conflicts between pastoralists and farmers, as these areas are suitable for both groups. Conversely, low SI\u003csub\u003eAgriculture\u003c/sub\u003e areas, which are mainly suitable for transhumance, show evidence of agricultural presence based on EO data. The EO data for all six agreement classes show the presence of croplands for SI\u003csub\u003eAgriculture\u003c/sub\u003e = 0 (Fig. 3)—these lands are suitable for seasonal agriculture, possibly used by farmers or pastoralists themselves.\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eFrom 2001 to 2010, conflicts were less frequent but concentrated in lands more suitable for transhumance [see Fig.\u0026nbsp;5(a)]. In these areas, there are sufficient resources to support livestock, and transhumance itself acts as a preventative mechanism unless there is competition from an immobile competitor (permanent farming or human settlement) or if the number of grazing livestock exceeds the carrying capacity of the land. As competition persisted on high-quality lands, it subsequently spread to lower quality lands suitable for transhumance. Similarly, the expansion of agricultural land by farmers may have occurred.\u003c/p\u003e\n \u003cp\u003eWe should consider that the second actor in pastoralist-related conflicts may be another pastoralist, farmers, settlers (in the built-up areas), or a military group. Suppose that these conflict pixels are located in cropland cultivated by farmers (and not by pastoralists). In this case, therefore, the conflict may be caused by the change in cropping timing, cropping pattern, the land use change from grassland to cropland by farmers, or the expansion of croplands by farmers in such a way they occupy the transhumance corridors (e.g., the number of farmers increases, and the area of cropland increases) which is already beyond the agricultural carrying capacity (SI = 0) for these lands. In this case, the conflict will increase with the same number of pastoralists and livestock (e.g., within the transhumance carrying capacity) or with an increasing number of pastoralists and livestock beyond the carrying capacity.\u003c/p\u003e\n \u003cp\u003eIf pastoralists cultivate these croplands they may have turned to farmers (or seasonal farmers). In that case, they are half-nomads with a fixed settlement. We guess that the increase in conflict numbers for this class is probably due to an increase in the pastoralist population or their livestock population beyond the seasonal carrying capacity for the fixed settlement. The exceeding of the carrying capacity by the population caused an inevitable environmental collapse (land degradation) and conflict in the half-nomad society (through the occurrence of conflict between the half-nomads using the land as cropland and the other pastoralists/half-nomads).\u003c/p\u003e\n \u003cp\u003eHowever, there is another likely scenario. It is possible that the amount of biomass has decreased due to the long-term effects of climate-related phenomena such as drought or climate change. In this case, even if the number of pastoralists or farmers remains constant and within the carrying capacity limits of a region, the amount of resources will decrease. This study can not address this probable scenario since the short period of 2001–2020 is insufficient for a climate-related assessment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.4. Limitations of the study\u003c/h2\u003e\n \u003cp\u003eThis study identifies potential transhumance corridors in SaSu and delineates seasonal suitability of transhumance based on water and biomass availability. However, several limitations should be acknowledged. The determination of specific paths chosen by pastoralists involves inherent uncertainties. Path selection is a dynamic and complex decision-making process influenced by numerous factors beyond environmental considerations such as water and biomass availability. These factors include livestock type, market destinations, seasonal weather conditions, herd size, travel distances, landmarks, tribal agreements and traditions, and interactions with local communities or government policies. As such, the corridors identified in this study reflect environmental suitability rather than capturing the full spectrum of pastoralist behaviors.\u003c/p\u003e\n \u003cp\u003eThe study assumes that monthly time-step data on water and vegetation cover, in combination with road proximity, provide sufficient spatiotemporal resolution for corridor identification. While this approach balances computational feasibility and ecological representation, using data with finer temporal resolution might capture greater variability in spatial water availability, potentially resulting in more scattered or fragmented corridor outputs.\u003c/p\u003e\n \u003cp\u003eRoad accessibility is modeled using Euclidean distance with a linear diminishing factor extending up to 30 km. While this parameterization provides a useful approximation, it does not account for non-linear diminishing effects, which may better reflect pastoralists’ preferences based on factors such as road quality, terrain, or livestock-specific needs. Moreover, the maximum distance threshold of 30 km might vary depending on specific pastoralist decisions or the demographics of their herds.\u003c/p\u003e\n \u003cp\u003eValidation of the identified corridors is another key challenge. The lack of observed GPS-tracked paths for pastoralists across the vast expanse of SaSu limits the ability to quantitatively assess the model’s accuracy. Current validation mainly relies on expert opinion and qualitative maps published in previous studies, which, while valuable, are inherently subjective. Acquiring more detailed and accurate tracking data is critical for refining the model and ensuring more robust validation in future research.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Methods","content":"\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the methodology employed in this research is elucidated through a flowchart that overviews the steps undertaken in the study. The subsequent sections of this paper will offer a detailed explanation of the method elements outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Case Study\u003c/h2\u003e \u003cp\u003eThe study area encompasses the semi-arid Sahelian and Sudanian (SaSu) zone\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, based on terrestrial ecoregions of the world\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e (See also \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oneearth.org/navigator/\u003c/span\u003e\u003cspan address=\"https://www.oneearth.org/navigator/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), between the Saharan desert and the humid Guinean zone, extending from the Atlantic coast to the Red Sea coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). SaSu is a vast region covering over 533.8\u0026nbsp;million hectares, characterized by diverse geography, including deserts, savannas, mountains, and coastal plains. This region represents a unique bioclimatic zone, hosting some of the last remaining intact wilderness areas globally and as a high-priority focus for wildlife conservation\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEncompassing 17 countries, SaSu is marked by Nigeria as the most populous, followed by Ethiopia. Water resources within SaSu exhibit significant heterogeneity, posing challenges regarding availability and distribution. Annual precipitation in the region varies from less than 50 mm close to the Sahara to exceeding 800 mm in certain parts beside Central Africa. Surface water and groundwater resources are limited, with varying accessibility across regions. SaSu is home to rural communities that are heavily reliant on the land for sustenance, particularly pastoralists. Human prosperity in this area is intricately linked to vegetation resources, given that approximately 80% of the rapidly growing population depends on traditional livelihood strategies, such as subsistence agriculture and livestock production via transhumance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Land suitability\u003c/h2\u003e \u003cp\u003eThis step aims to identify areas suitable for transhumance\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and agriculture\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The agricultural suitability assessment is based on the previous studies, incorporating fuzzy membership functions aligned with FAO recommendations\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Both assessments utilize more recent EO data sources\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Overlaying various geospatial information layers enables the determination of the Suitability Index (SI) map. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the layers employed in this process. The SI values standardize the data into a scaled range from 0 (not suitable) to 1 (very suitable), with excluded areas marked by SI\u0026thinsp;=\u0026thinsp;0. Fuzzy membership functions for transhumance and agriculture suitability are illustrated in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure S2, respectively.\u003c/p\u003e \u003cp\u003eThe final SI\u003csub\u003eTranshumance\u003c/sub\u003e index is the mean of six SI layers for transhumance suitability\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Each input variable\u0026rsquo;s SI map is calculated using the respective EO layer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and its fuzzy membership function (Figrue S1). For agricultural suitability, the final SI\u003csub\u003eAgriculture\u003c/sub\u003e index is computed following Liebig\u0026rsquo;s law of minimum for all 11 calculated SI layers\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and their fuzzy membership functions (Figure S2). Finally, the SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e layers are classified into suitability classes\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, as unsuitable (SI\u0026thinsp;=\u0026thinsp;0), very poor (0\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.2), poor (0.2\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.4), medium (0.4\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.6), good (0.6\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;0.8), and very good (0.8\u0026thinsp;\u0026lt;\u0026thinsp;SI\u0026thinsp;\u0026le;\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Geospatial data for suitability analysis\u003c/h2\u003e \u003cp\u003eThe data processing for this part is performed and all figures were generated in the Google Earth Engine (GEE) platform. To this goal, geospatial data and layers are aggregated from diverse sources, comprising 24 datasets. These encompass information on the water availability index, vegetation cover, land cover, climate, soil properties, topography, excluded areas, and validation datasets. Comprehensive details regarding the utilized data, along with their descriptions and sources, are available in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1. Geospatial data for transhumance suitability\u003c/h2\u003e \u003cp\u003eThe assessment of surface water availability employed the BioHydroGenerator v.4.3 tool\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, utilizing the Small Water Bodies product from Copernicus Global Land Service (CGLS) as a primary input. To evaluate surface water accessibility, BioHydroGenerator establishes a 30 km buffer ring around identified cells, prioritizing them using a decreasing Gaussian weighting function as the water accessibility index (WAI), ranging from 0 to 1 as follows.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:WAI\\left(d\\right)=\\left(1-{F}_{BG}\\right)\\times\\:\\text{exp}\\left(-\\frac{{d}^{2}}{2\\times\\:{\\sigma\\:}^{2}}\\right)+{F}_{BG}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\)\u003c/span\u003e\u003c/span\u003e represents the distance to the water point in kilometers, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e denotes a parameter of the Gaussian function adjusted to extend 1% beyond 30 km (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\:=\\:\\frac{30}{\\sqrt{2\\times\\:Ln\\left(100\\right)}}\\)\u003c/span\u003e\u003c/span\u003e). Additionally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{BG}\\)\u003c/span\u003e\u003c/span\u003e corresponds to the background WAI, which varies based on aridity zones\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, showcasing a gradual transition from 0% in hyper-arid areas to 100% in humid regions. The WAI scale spans from 0 to 1, where 0 signifies no access to water, and 1 indicates the presence of a permanent water point \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. WAI layers are generated monthly throughout the 2001\u0026ndash;2020 period.\u003c/p\u003e \u003cp\u003eAdditionally, the Vegetation Cover Index (VCI) and Road Accessibility Index (RAI) were calculated. Based on MODIS acquisitions, the VCI layers were derived from Total Vegetation Cover Products from GEOGLAM-RAPP, encompassing monthly vegetation coverage fractions, including green and dry vegetation.\u003c/p\u003e \u003cp\u003eThe road layer was obtained from the GADM database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gadm.org/\u003c/span\u003e\u003cspan address=\"https://www.gadm.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the urban land cover class was extracted from global land cover data\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. The Euclidean distance to roads and urban areas was considered, incorporating the road/urban area accessibility index (RAI) as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:RAI\\left(d\\right)=\\left\\{\\begin{array}{cc}\\frac{30-d}{30}\u0026amp;\\:d\\le\\:30\\\\\\:0\u0026amp;\\:d\u0026gt;30\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere 30 km refers to the maximum distance from the roads. Inner urban areas were treated as unsuitable for transhumance corridors, indicated by zero suitability as excluded areas. Urban and road layers were considered static for the modeling period (2001 to 2020). For both water access and road access layers, the 30 km buffer distance is based on a maximum of two days of walking to access a water resource\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The final SI\u003csub\u003eTranshumance\u003c/sub\u003e index (0\u0026ndash;1) is the mean of WAI, VCI, RAI layers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2. Geospatial data for agriculture suitability\u003c/h2\u003e \u003cp\u003eEight variables are included in the calculation of SI\u003csub\u003eAgriculture\u003c/sub\u003e: precipitation, available soil water content, soil texture, coarse soil fragments, soil pH, soil cation exchange capacity (CEC), soil organic carbon (OC), and slope. We also excluded two land uses: road/built areas and water bodies. We did not exclude protected areas and forests because the EO data show that farmers violate these protections in several places (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Given the data constraints for Africa, several simplifications are introduced in comparison to the original reference\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The first simplification pertains to variables related to soil suitability, including soil salinity, sodicity, base saturation, and the percentage of gypsum, which are omitted for the overall soil suitability, adopting a more optimistic approach. Another simplification mentioned in the original reference\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e relates to irrigated croplands. This method primarily focuses on long-term agriculture full-year cropping, such as agriculture without the need for irrigation, reliant on precipitation as the primary water source, neglecting the complexities of irrigation where water delivery mechanisms are unknown.\u003c/p\u003e \u003cp\u003eThis study does not consider climate change impacts, including shifts in growing seasons and precipitation timing. Freezing point effects on agricultural suitability are also simplified, adopting a more optimistic stance toward the climate component of agriculture. In the SaSu region, where precipitation is already limited, the effects of temperature on the growing season are not considered, contributing to a more optimistic perspective on the climate component of agriculture.\u003c/p\u003e \u003cp\u003eThis study assumes that African farmers are dynamic agents capable of adapting to changing environments. Consequently, inter-annual variations in precipitation timing over the years are not considered, and calculations for agriculture suitability rely on long-term average data. In contrast, transhumance is inherently seasonal, leading to the calculation of two water availability and vegetation cover datasets for transhumance suitability on a monthly time scale. However, each month\u0026rsquo;s average is used based on acquired data from 2001 to 2020.\u003c/p\u003e \u003cp\u003eTemporal aspect is crucial in agriculture to account for changes in land use over time. Based on the cropland agreement layer, one of the six datasets (GFSAD1km) represents the extent of cropland for 2010. Some areas designated as suitable for agriculture may have changed to other land uses. Another dataset (ESA landcover), representing land use for 2020, has a much smaller area than the 2010 layer. We assume that suitable agricultural land, in a static form without temporal variations due to drought or climate change, represents land that is cultivated permanently (irrigated) or seasonally (rainfed) during a given period. To account for this, we utilized the cropland agreement layer with agreement level\u0026thinsp;\u0026ge;\u0026thinsp;1. However, it is essential to acknowledge that this combination lacks a long-term accuracy assessment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of RS/GIS data used for the transhumance suitability analysis of SaSu region. GEE refers to Google Earth Engine\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLayer No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource (Reference) or GEE layer name\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMain 6 suitability layers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly water availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioHydroGenerator\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly vegetation coverage fraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGEOGLAM-RAPP, based on MODIS acquisitions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfrica tree cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.7764460\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7764460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e74,75\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gadm.org/\u003c/span\u003e\u003cspan address=\"https://www.gadm.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eee.ImageCollection(\"ESA/WorldCover/v100\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS layer provided by NASA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNASA\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eee.ImageCollection(\"MODIS/006/MOD17A3HGF\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAuxialary layers to produce the maps\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdministrative borders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge Scale International Boundaries\u003c/p\u003e \u003cp\u003eee.FeatureCollection(\"USDOS/LSIB_SIMPLE/2017\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInland water bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eee.ImageCollection(\"ESA/WorldCover/v100\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCroplands agreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.7244124\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7244124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e44,55\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation layers for transhumance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePastoralist\u0026rsquo;s conflicts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAn Armed Conflict Location and Event Dataset (ACLED)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022343310378914\u003c/span\u003e\u003cspan address=\"10.1177/0022343310378914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePastoralist\u0026rsquo;s transhumance corridors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 Maps in the published reports and resources\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of RS/GIS data used for the suitability analysis of SaSu for agriculture. GEE refers to Google Earth Engine\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLayer No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource (Reference) or GEE layer name\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eClimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean annual precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSoil properties\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH (H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.Image(\"OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCation Exchange Capacity (cmol\u003csub\u003ec\u003c/sub\u003e/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.Image(\"ISDASOIL/Africa/v1/cation_exchange_capacity\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic carbon (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.Image(\"ISDASOIL/Africa/v1/carbon_organic\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoarse fragments (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.Image(\"ISDASOIL/Africa/v1/stone_content\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.Image(\"ISDASOIL/Africa/v1/texture_class\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailable Water Content (mm/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.Image(\"OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTopography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNASA SRTM Digital Elevation 30 m\u003c/p\u003e \u003cp\u003eee.Image(\"USGS/SRTMGL1_003\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExcluded areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInland water bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eee.ImageCollection(\"ESA/WorldCover/v100\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtected areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.protectedplanet.net\u003c/span\u003e\u003cspan address=\"https://www.protectedplanet.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e70\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eee.FeatureCollection(\"WCMC/WDPA/current/polygons\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eee.ImageCollection(\"ESA/WorldCover/v100\")\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation layers for agriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCroplands agreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.7244124\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7244124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e44,55\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSuitability index for nutrient availability, rooting conditions, and workability variables as a lookup function of USDA\u0026rsquo;s soil textures. Values are based on FAO\u0026rsquo;s recommendation\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNutrient availability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRooting conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorkability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay (heavy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilty clay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilty clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandy clay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoamy sand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3. Geospatial validation data\u003c/h2\u003e \u003cp\u003eMultiple datasets involve validating the transhumance corridors, pastoralist-related conflicts, and agricultural lands. Validation data for transhumance corridors comprises two main groups. The first group covers the northwest of Africa, providing insights into migration corridors observed by pastoralists, as documented in one reference\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The second group results from an exhaustive literature review, analyzing more than 50 records and extracting 36 maps depicting transhumance corridors. While these maps serve as valuable qualitative sources, they inherently carry spatial uncertainty regarding the precise location of the corridors.\u003c/p\u003e \u003cp\u003eFor pastoralist-related conflicts and the SI\u003csub\u003eTranshumance\u003c/sub\u003e validation, the Armed Conflict Location and Event Data Project\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (ACLED) is the primary validation dataset, covering the period from 2001 to 2020 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.acleddata.com/\u003c/span\u003e\u003cspan address=\"https://www.acleddata.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset includes conflict locations where one or both actors are identified as pastoralists. This dataset filters conflicts involving at least one pastoralist actor, resulting in a total of 4372 conflict points from 2001 to 2020. In the validation process, the point layer is converted to a raster layer, with each conflict point represented by a circle with a 500 m radius circle (500 m is chosen based on processing limitations and the number of pixels limit for SaSu in GEE). The raster analysis performed with a 500 m \u0026times; 500 m pixel size results in 8940 pixels representing conflicts.\u003c/p\u003e \u003cp\u003eValidation data for agricultural lands relies on the cropland agreement dataset\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This dataset offers geospatial information on the agreement-disagreement classifications of six open-access high-resolution cropland maps derived from remote sensing. Each point in this dataset is ranked from 6 to 0, where 6 signifies agreement among all six sources on the cropland, and 0 indicates unanimous agreement on the non-cropland characteristic of the land.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. The logic behind the transhumance corridors and possible conflict zones\u003c/h2\u003e \u003cp\u003eThe pastoralist lifestyle is based on the livestock and feeding the animals. Therefore, the main bottlenecks for pastoralists are environmental factors\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Among the six considered factors (water, vegetation cover, green cover, forest cover, roads, and urban area)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, water is the primary environmental limiting factor, followed by vegetation cover (as the two supply factors in the animal life cycle), and access to the markets (vicinity of population settlements like roads and cities) is the primary social limiting factor (as a demand factor for the animal life cycle).\u003c/p\u003e \u003cp\u003eTherefore, in the mind of pastoralists, a transhumance corridor compromises supply and demand for their livestock while providing water and feed on the path. Pastoralist knows their transhumance paths by heart and uses multi-generational experience. Due to the institutional shaping of this knowledge, they persistently use the same transhumance path every year\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. This compromise can be modeled by inter-annual averaging water access and vegetation coverage on monthly time steps and road/urban access as long-term static layers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Also, the suitability gradient over months shows the path from high SI\u003csub\u003eTranshumance\u003c/sub\u003e to low SI\u003csub\u003eTranshumance\u003c/sub\u003e, similar to flow routing based on topography, resulting in transhumance corridors. The transhumance corridor network is quantified using spatial analysis, based on the assumption that pastoralists move from less suitable to more suitable areas in their vicinity due to seasonal changes. The SI\u003csub\u003eTranshumance\u003c/sub\u003e map, developed using the Google Earth Engine (GEE), is subjected to further analysis in the ArcGIS environment. Although this method shows the hotspots suitable for wet and dry seasons and the optimal path to connect these suitable hotspots, this method doesn\u0026rsquo;t consider the definite start, end and distance of transhumance for a group of pastoralists. Therefore, a transhumance corridor in this study shows the passage and not the itinerary. Therefore, classifying the pastoralists based on their behavior for moving is not possible with the current study.\u003c/p\u003e \u003cp\u003eThere are eight cases for farmer-herder conflict\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e; (1) Resource competition: Land, grazing, and water rivalry (68%); (2) Cattle or crop damage: Trampling, rustling, blocking, pollution (24%); (3) Intergroup animosity: Social, religious, and cultural hostilities; (4) Migration: Climate-induced, historical patterns, ethnic tensions; (5) Land tenure insecurity: Ambiguous laws, contested rights, insecurity; (6) State weakness: Weak policies, lack of action, government support; (7) Nonstate armed groups: Affiliation, attacks, self-defense; and (8) Historical patterns: Colonial evictions, policy impact, grazing conflicts. Researchers\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e identified resource competition as the primary cause of conflict, especially for arable land, grazing land, and water access. Therefore, a potential conflict zone is already occupied by farmers as croplands (either irrigated or rainfed) located on the transhumance corridor. This conditional probability function can estimate the probability of the pastoralist-farmer conflict:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Fuzzy}_{Conflict}\\left(x\\right)={Fuzzy\\:SI}_{Agriculture}\\left(x\\right|x\\in\\:Agreed\\:Cropland)\\times\\:{Fuzzy\\:SI}_{Transhumance}(x)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Fuzzy}_{Conflict}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e is the fuzzy probability of conflict for the location \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Fuzzy\\:SI}_{Agriculture}\\left(x\\right|x\\in\\:Agreed\\:Cropland)\\)\u003c/span\u003e\u003c/span\u003e is the fuzzy probability function of conflict based on SI\u003csub\u003eAgriculture\u003c/sub\u003e for location \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e if it is a cropland pixel based on the cropland agreement layer, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Fuzzy\\:SI}_{Transhumance}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e is the fuzzy probability function of conflict based on SI\u003csub\u003eTranshumance\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThis analysis uses two derived fuzzy functions [Figure S3(c, d)] to fuzzify the SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e maps. The first fuzzy function for SI\u003csub\u003eTranshumance\u003c/sub\u003e is based on the distribution of conflict points over 2001\u0026ndash;2020. The second fuzzy function is based on the SI\u003csub\u003eAgriculture\u003c/sub\u003e and the distribution of conflict points in the suitability classes for the same period. Based on the two fuzzy functions, the SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e are mapped to their fuzzy versions with conflict scores from 0 to 1. The overall fuzzy map for conflicts is obtained by multiplying the two fuzzy maps.\u003c/p\u003e \u003cp\u003eIt is worth mentioning that this method calculated the probability by considering environmental factors affecting SI\u003csub\u003eTranshumance\u003c/sub\u003e and SI\u003csub\u003eAgriculture\u003c/sub\u003e. The complete conflict probability estimate is beyond the competition for water and food resources since it is a function of combined environmental, social, demographic, behavioral, and economic drivers contingent on multiple short-term and long-term social institutions like politics and history. At the same time, conflict is just one of the resulting encounters between farmers and herders in an \u0026ldquo;aggressive\u0026rdquo; manner\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. There are \u0026ldquo;passive\u0026rdquo; and \u0026ldquo;constructive\u0026rdquo; encounters\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, which are beyond this study\u0026rsquo;s scope.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Pioneer Center for Landscape Research in Sustainable Agricultural Futures (Land-CRAFT), DNRF grant number P2, Aarhus, Denmark, and Institut National de la Recherche Scientifique (INRS), Quebec, Canada. The authors would like to thank Professor Andr\u0026eacute; St-Hilaire for providing the opportunity to conduct this research as a collaborative research internship for Mostafa Khorsandi. We thank G\u0026uuml;lnur Dogan (Ph.D.), Center Manager of Land-CRAFT for her kind support of this project. Furthermore, we thank INRS for granting Mostafa Khorsandi the International Mobility and Short Stays Outside Quebec Program (PMICSE) in 2023.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are publicly available on the Google Earth Engine data catalog.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eThe codes to create agricultural suitability layer are available here:\u003c/p\u003e\n\u003cp\u003ehttps://code.earthengine.google.com/191a60c504884dee8649e3b024a51043\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, the codes to create the transhumance suitability layer and transhumance corridors are available here:\u003c/p\u003e\n\u003cp\u003ehttps://code.earthengine.google.com/17ac3801375e6061603e43f2dbb64ca8\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eMK, K.B.-B., and JR conceived and designed the study. MK and EF preprocessed the data, and MK performed the modeling and analyses. MK wrote the first draft with support from JR, while other co-authors (E.F., A.S., and K.B.-B.) contributed to improving the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchlolz, F. \u0026amp; Schlee, G. in \u003cem\u003eInternational Encyclopedia of the Social \u0026amp; Behavioral Sciences (Second Edition)\u003c/em\u003e (ed James D. Wright) 838-843 (Elsevier, 2015).\u003c/li\u003e\n\u003cli\u003eLees, S. H. \u0026amp; Bates, D. G. The Origins of Specialized Nomadic Pastoralism: A Systemic Model. \u003cem\u003eAmerican Antiquity\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 187-193 (1974). https://doi.org/10.2307/279581\u003c/li\u003e\n\u003cli\u003eStenning, D. J. Transhumance, migratory drift, migration; patterns of pastoral Fulani nomadism. \u003cem\u003eThe Journal of the Royal Anthropological Institute of Great Britain and Ireland\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 57-73 (1957).\u003c/li\u003e\n\u003cli\u003eJacquemot, P. Pastoralism in Africa A way of life in danger? (2023).\u003c/li\u003e\n\u003cli\u003eMeadows, D. H., Randers, J. \u0026amp; Meadows, D. A Synopsis: Limits to Growth: The 30-Year Update. \u003cem\u003eEstados Unidos: Chelsea Green Publishing Company\u003c/em\u003e \u003cstrong\u003e381\u003c/strong\u003e (2004).\u003c/li\u003e\n\u003cli\u003eMeadows, D. H., Goldsmith, E. I. \u0026amp; Meadows, P. The limits to growth. (Earth Island Limited London, 1972).\u003c/li\u003e\n\u003cli\u003eScheper, C.\u003cem\u003e et al.\u003c/em\u003e The role of agro-ecological factors and transboundary transhumance in shaping the genetic diversity in four indigenous cattle populations of Benin. \u003cem\u003eJournal of Animal Breeding and Genetics\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 622-640 (2020). https://doi.org/https://doi.org/10.1111/jbg.12495\u003c/li\u003e\n\u003cli\u003eSchwarz, M.\u003cem\u003e et al.\u003c/em\u003e Assessing the Environmental Suitability for Transhumance in Support of Conflict Prevention in the Sahel. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1109 (2022).\u003c/li\u003e\n\u003cli\u003eDuporge, I.\u003cem\u003e et al.\u003c/em\u003e A satellite perspective on the movement decisions of African elephants in relation to nomadic pastoralists. \u003cem\u003eRemote Sensing in Ecology and Conservation\u003c/em\u003e (2022).\u003c/li\u003e\n\u003cli\u003eHouessou, S. O.\u003cem\u003e et al.\u003c/em\u003e The role of cross-border transhumance in influencing resident herders\u0026rsquo; cattle husbandry practices and use of genetic resources. \u003cem\u003eAnimal\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2378-2386 (2020). https://doi.org/https://doi.org/10.1017/S1751731120001378\u003c/li\u003e\n\u003cli\u003eTurner, M. D. \u0026amp; Schlecht, E. Livestock mobility in sub-Saharan Africa: A critical review. \u003cem\u003ePastoralism\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 13 (2019). https://doi.org/10.1186/s13570-019-0150-z\u003c/li\u003e\n\u003cli\u003eMotta, P.\u003cem\u003e et al.\u003c/em\u003e Cattle transhumance and agropastoral nomadic herding practices in Central Cameroon. \u003cem\u003eBMC veterinary research\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1-12 (2018).\u003c/li\u003e\n\u003cli\u003eLuizza, M. Transhumant Pastoralism in Central Africa: Emerging Impacts on Conservation and Security. \u003cem\u003eUnpublished report. US Fish \u0026amp; Wildlife Service, Washington, DC, USA\u003c/em\u003e (2017).\u003c/li\u003e\n\u003cli\u003eN.A. Pastoralist and Farmer-Herder Conflicts in the Sahel. \u003cem\u003eClimate Diplomacy\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eJobbins, M. \u0026amp; McDonnell, A. Pastoralism and conflict: Tools for prevention and response in the Sudano-Sahel. \u003cem\u003eSearch for Common Ground\u003c/em\u003e, 1-110 (2021).\u003c/li\u003e\n\u003cli\u003eBrottem, L. Growing Complexity of Farmer-Herder Conflict in West and Central Africa. (2021).\u003c/li\u003e\n\u003cli\u003eKr\u0026auml;tli, S. \u0026amp; Toulmin, C. \u003cem\u003eFarmer-herder conflict in sub-Saharan Africa?\u003c/em\u003e , (International Institute for Environment and Development (IIED) London, UK, 2020).\u003c/li\u003e\n\u003cli\u003eLoveridge, A. J.\u003cem\u003e et al.\u003c/em\u003e Bells, bomas and beefsteak: complex patterns of human-predator conflict at the wildlife-agropastoral interface in Zimbabwe. \u003cem\u003ePeerJ\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e2898 (2017).\u003c/li\u003e\n\u003cli\u003eZhongming, Z., Linong, L., Xiaona, Y., Wangqiang, Z. \u0026amp; Wei, L. Livelihood security: Climate change, migration and conflict in the Sahel. (2011).\u003c/li\u003e\n\u003cli\u003eAdams, E. A., Thill, A., Kuusaana, E. D. \u0026amp; Mittag, A. Farmer\u0026ndash;herder conflicts in sub-Saharan Africa: drivers, impacts, and resolution and peacebuilding strategies. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 123001 (2023). https://doi.org/10.1088/1748-9326/ad0702\u003c/li\u003e\n\u003cli\u003eRaleigh, C., Linke, r., Hegre, H. \u0026amp; Karlsen, J. Introducing ACLED: An Armed Conflict Location and Event Dataset. \u003cem\u003eJournal of Peace Research\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 651-660 (2010). https://doi.org/10.1177/0022343310378914\u003c/li\u003e\n\u003cli\u003eTerfa, B. K. \u0026amp; Suryabhagavan, K. V. Rangeland suitability evaluation for livestock production using remote sensing and GIS techniques in dire district, southern Ethiopia. \u003cem\u003eGlobal Journal of Science Frontier Research: H Environment \u0026amp; Earth Science\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2015).\u003c/li\u003e\n\u003cli\u003eSensing, N. U. R. Rangeland suitability for livestock grazing and economic implications in irepodun area of Osun state Nigeria using remote sensing and GIS techniques. (2016).\u003c/li\u003e\n\u003cli\u003eStrohbach, B. J. Making more of vegetation classification results: a livestock farming Suitability Index as tool for land-use planning in Namibia. \u003cem\u003ePhytocoenologia\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 7-22 (2018).\u003c/li\u003e\n\u003cli\u003eBalew, A.\u003cem\u003e et al.\u003c/em\u003e Identification of Suitable Land for Livestock Production Using GIS-Based Multicriteria Decision Analysis and Remote Sensing in the Bale Lowlands, Ethiopia. \u003cem\u003eInternational Journal of Ecology\u003c/em\u003e \u003cstrong\u003e2022\u003c/strong\u003e, 9585552 (2022). https://doi.org/10.1155/2022/9585552\u003c/li\u003e\n\u003cli\u003eGebeyehu, A. K., Sonneveld, B. G. J. S. \u0026amp; Snelder, D. J. Identifying Hotspots of Overgrazing in Pastoral Areas: Livestock Mobility and Fodder Supply\u0026ndash;Demand Balances in Nyangatom, Lower Omo Valley, Ethiopia. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 3260 (2021).\u003c/li\u003e\n\u003cli\u003eFarazmand, A., Arzani, H., Javadi, S. \u0026amp; Sanadgol, A. Determining the factors affecting rangeland suitability for livestock and wildlife grazing. \u003cem\u003eApplied Ecology \u0026amp; Environmental research\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 317-329 (2019).\u003c/li\u003e\n\u003cli\u003eKarlson, M. \u0026amp; Ostwald, M. Remote sensing of vegetation in the Sudano-Sahelian zone: A literature review from 1975 to 2014. \u003cem\u003eJournal of Arid Environments\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e, 257-269 (2016).\u003c/li\u003e\n\u003cli\u003eJahnke, H. E. \u0026amp; Jahnke, H. E. \u003cem\u003eLivestock production systems and livestock development in tropical Africa\u003c/em\u003e. Vol. 35 (Kieler Wissenschaftsverlag Vauk Kiel, 1982).\u003c/li\u003e\n\u003cli\u003eKhorsandi, M., Homayouni, S. \u0026amp; van Oel, P. The edge of the petri dish for a nation: Water resources carrying capacity assessment for Iran. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e817\u003c/strong\u003e, 153038 (2022). https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.153038\u003c/li\u003e\n\u003cli\u003eRunning, S. W. A regional look at HANPP: human consumption is increasing, NPP is not. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 111003 (2014).\u003c/li\u003e\n\u003cli\u003eSircely, J., Conant, R. T. \u0026amp; Boone, R. B. Simulating Rangeland Ecosystems with G-Range: Model Description and Evaluation at Global and Site Scales. \u003cem\u003eRangeland Ecology \u0026amp; Management\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 846-857 (2019). https://doi.org/https://doi.org/10.1016/j.rama.2019.03.002\u003c/li\u003e\n\u003cli\u003eRahimi, J.\u003cem\u003e et al.\u003c/em\u003e Beyond livestock carrying capacity in the Sahelian and Sudanian zones of West Africa. \u003cem\u003eScientific reports\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1-15 (2021).\u003c/li\u003e\n\u003cli\u003eMaman Moutari, E. \u0026amp; Giraut, F. Le corridor de transhumance au Sahel : un arch\u0026eacute;type de territoire multisitu\u0026eacute; ? \u003cem\u003eL\u0026rsquo;Espace g\u0026eacute;ographique\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 306-323 (2013). https://doi.org/10.3917/eg.424.0306\u003c/li\u003e\n\u003cli\u003eMesgaran, M. B., Madani, K., Hashemi, H. \u0026amp; Azadi, P. Iran\u0026rsquo;s land suitability for agriculture. \u003cem\u003eScientific reports\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 7670 (2017).\u003c/li\u003e\n\u003cli\u003eBaskaran, V., Madasamy, M., Kumar, S. P. \u0026amp; Sahana, S. V. Modeling the land suitability for agricultural utility in a semi-arid region of Tirunelveli district, South India using multi-criteria and geospatial approach. \u003cem\u003eModeling Earth Systems and Environment\u003c/em\u003e (2023). https://doi.org/10.1007/s40808-023-01706-5\u003c/li\u003e\n\u003cli\u003eFischer, G.\u003cem\u003e et al.\u003c/em\u003e Global Agro-ecological Zones (GAEZ v4)-Model Documentation. (2021).\u003c/li\u003e\n\u003cli\u003eZabel, F., Putzenlechner, B. \u0026amp; Mauser, W. Global Agricultural Land Resources \u0026ndash; A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e107522 (2014). https://doi.org/10.1371/journal.pone.0107522\u003c/li\u003e\n\u003cli\u003eVan Ittersum, M. K.\u003cem\u003e et al.\u003c/em\u003e Can sub-Saharan Africa feed itself? \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 14964-14969 (2016).\u003c/li\u003e\n\u003cli\u003eMberu, B. U. \u0026amp; Ezeh, A. C. The population factor and economic growth and development in Sub-Saharan African countries. \u003cem\u003eAfrican Population Studies\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e (2017).\u003c/li\u003e\n\u003cli\u003eEzeh, A., Kissling, F. \u0026amp; Singer, P. Why sub-Saharan Africa might exceed its projected population size by 2100. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e396\u003c/strong\u003e, 1131-1133 (2020).\u003c/li\u003e\n\u003cli\u003eRahimi, J., Smerald, A., Moutahir, H., Khorsandi, M. \u0026amp; Butterbach-Bahl, K. The potential consequences of grain-trade disruption on food security in the Middle East and North Africa region. \u003cem\u003eFrontiers in Nutrition\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2023). https://doi.org/https://doi.org/10.3389/fnut.2023.1239548\u003c/li\u003e\n\u003cli\u003eNassef, M., Eba, B., Islam, K., Djohy, G. \u0026amp; Flintan, F. E. Causes of farmer\u0026ndash;herder conflicts in Africa: A systematic scoping review. (2023).\u003c/li\u003e\n\u003cli\u003eTubiello, F. N.\u003cem\u003e et al.\u003c/em\u003e A new cropland area database by country circa 2020. \u003cem\u003eEarth Syst. Sci. Data\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 4997-5015 (2023). https://doi.org/10.5194/essd-15-4997-2023\u003c/li\u003e\n\u003cli\u003eMbih, R. A., Ndzeidze, S. K., Wanyama, D. \u0026amp; Mbuh, M. J. Challenges of transhumance in Northwest Cameroon. \u003cem\u003eSN Social Sciences\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 208 (2022). https://doi.org/10.1007/s43545-022-00515-4\u003c/li\u003e\n\u003cli\u003eOuedraogo, A. S.\u003cem\u003e et al.\u003c/em\u003e Cross border transhumance involvement in ticks and tick-borne pathogens dissemination and first evidence of Anaplasma centrale in Burkina Faso. \u003cem\u003eTicks and Tick-borne Diseases\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 101781 (2021). https://doi.org/https://doi.org/10.1016/j.ttbdis.2021.101781\u003c/li\u003e\n\u003cli\u003eL\u0026eacute;onard, U. B., MOUSSA. The Dynamics and Impacts of Transhumance and Neo-Pastoralism on Biodiversity, Local Communities and Security: Congo Basin. (2021).\u003c/li\u003e\n\u003cli\u003eTugjamba, N., Walkerden, G. \u0026amp; Miller, F. Adapting nomadic pastoralism to climate change. \u003cem\u003eClimatic Change\u003c/em\u003e \u003cstrong\u003e176\u003c/strong\u003e, 28 (2023). https://doi.org/10.1007/s10584-023-03509-0\u003c/li\u003e\n\u003cli\u003eOpitz-Stapleton, S. Transboundary climate risks to african dryland livestock economies. (2023).\u003c/li\u003e\n\u003cli\u003eWardropper, C. B.\u003cem\u003e et al.\u003c/em\u003e Improving rangeland climate services for ranchers and pastoralists with social science. \u003cem\u003eCurrent Opinion in Environmental Sustainability\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 82-91 (2021). https://doi.org/https://doi.org/10.1016/j.cosust.2021.07.001\u003c/li\u003e\n\u003cli\u003eSimpson, N. P.\u003cem\u003e et al.\u003c/em\u003e A framework for complex climate change risk assessment. \u003cem\u003eOne Earth\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 489-501 (2021). https://doi.org/https://doi.org/10.1016/j.oneear.2021.03.005\u003c/li\u003e\n\u003cli\u003eRahimi, J.\u003cem\u003e et al.\u003c/em\u003e A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africa\u0026rsquo;s drylands. \u003cem\u003eNature Food\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 523-531 (2022). https://doi.org/10.1038/s43016-022-00543-6\u003c/li\u003e\n\u003cli\u003eAbdi, A., Seaquist, J., Tenenbaum, D., Eklundh, L. \u0026amp; Ard\u0026ouml;, J. The supply and demand of net primary production in the Sahel. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 094003 (2014).\u003c/li\u003e\n\u003cli\u003eBhaumik, S. K. \u0026amp; Nugent, J. B. Analysis of Food Demand in Peru: Implications for Food\u0026ndash;Feed Competition. \u003cem\u003eReview of Development Economics\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 242-257 (1999). https://doi.org/https://doi.org/10.1111/1467-9361.00065\u003c/li\u003e\n\u003cli\u003eTubiello, F. N.\u003cem\u003e et al.\u003c/em\u003e (Zenodo, 2022).\u003c/li\u003e\n\u003cli\u003eCobbing, J. \u0026amp; Hiller, B. Waking a sleeping giant: Realizing the potential of groundwater in Sub-Saharan Africa. \u003cem\u003eWorld Development\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e, 597-613 (2019).\u003c/li\u003e\n\u003cli\u003eFaramarzi, M.\u003cem\u003e et al.\u003c/em\u003e Modeling impacts of climate change on freshwater availability in Africa. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e480\u003c/strong\u003e, 85-101 (2013). https://doi.org/https://doi.org/10.1016/j.jhydrol.2012.12.016\u003c/li\u003e\n\u003cli\u003eMacDonald, A. M., Bonsor, H. C., Dochartaigh, B. \u0026Eacute;. \u0026Oacute;. \u0026amp; Taylor, R. G. Quantitative maps of groundwater resources in Africa. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 024009 (2012).\u003c/li\u003e\n\u003cli\u003eDossouhoui, G. I. A., Yemadje, P. L., Diogo, R. V. C., Balarabe, O. \u0026amp; Tittonell, P. \u0026ldquo;Sedentarisation\u0026rdquo; of transhumant pastoralists results in privatization of resources and soil fertility decline in West Africa\u0026apos;s cotton belt. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (2023). https://doi.org/10.3389/fsufs.2023.1120315\u003c/li\u003e\n\u003cli\u003eAssogba, G. G. C., Berre, D., Adam, M. \u0026amp; Descheemaeker, K. Can low-input agriculture in semi-arid Burkina Faso feed its soil, livestock and people? \u003cem\u003eEuropean Journal of Agronomy\u003c/em\u003e \u003cstrong\u003e151\u003c/strong\u003e, 126983 (2023). https://doi.org/https://doi.org/10.1016/j.eja.2023.126983\u003c/li\u003e\n\u003cli\u003eKhorsandi, M., Omidi, T. \u0026amp; van Oel, P. Water-related limits to growth for agriculture in Iran. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e16132 (2023). https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e16132\u003c/li\u003e\n\u003cli\u003eKhorsandi, M., Bateni, M. M. \u0026amp; Van Oel, P. A mathematical meta-model for assessing the self-sufficient water resources carrying capacity across different spatial scales in Iran. \u003cem\u003eHeliyon\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eKarra, K.\u003cem\u003e et al.\u003c/em\u003e in \u003cem\u003e2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.\u003c/em\u003e 4704-4707 (IEEE).\u003c/li\u003e\n\u003cli\u003eSpringer, A., Lopez, T., Owor, M., Frappart, F. \u0026amp; Stieglitz, T. The Role of Space-Based Observations for Groundwater Resource Monitoring over Africa. \u003cem\u003eSurveys in Geophysics\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 123-172 (2023). https://doi.org/10.1007/s10712-022-09759-4\u003c/li\u003e\n\u003cli\u003eCuthbert, M. O.\u003cem\u003e et al.\u003c/em\u003e Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e572\u003c/strong\u003e, 230-234 (2019). https://doi.org/10.1038/s41586-019-1441-7\u003c/li\u003e\n\u003cli\u003eBonsor, H., Shamsudduha, M., Marchant, B., Macdonald, A. M. \u0026amp; Taylor, R. Seasonal and decadal groundwater changes in African sedimentary aquifers estimated using GRACE products and LSMs. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 904 (2018).\u003c/li\u003e\n\u003cli\u003eVerkaik, J., Sutanudjaja, E. H., Oude Essink, G. H. P., Lin, H. X. \u0026amp; Bierkens, M. F. P. GLOBGM v1.0: a parallel implementation of a 30\u0026amp;thinsp;arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. \u003cem\u003eGeosci. Model Dev.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 275-300 (2024). https://doi.org/10.5194/gmd-17-275-2024\u003c/li\u003e\n\u003cli\u003eSouverijns, N.\u003cem\u003e et al.\u003c/em\u003e Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3817 (2020).\u003c/li\u003e\n\u003cli\u003eOlson, D. M.\u003cem\u003e et al.\u003c/em\u003e Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. \u003cem\u003eBioScience\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 933-938 (2001). https://doi.org/10.1641/0006-3568(2001)051[0933:Teotwa]2.0.Co;2\u003c/li\u003e\n\u003cli\u003eUNEP-WCMC \u0026amp; IUCN. (ed UNEP-WCMC and IUCN) (Cambridge, UK: UNEP-WCMC and IUCN, 2024).\u003c/li\u003e\n\u003cli\u003eHengl, T.\u003cem\u003e et al.\u003c/em\u003e African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y\u003c/li\u003e\n\u003cli\u003eFillol, E. Biohydrogenerator User Guide. (2018).\u003c/li\u003e\n\u003cli\u003eZanaga, D.\u003cem\u003e et al.\u003c/em\u003e ESA WorldCover 10 m 2020 v100. (2021). https://doi.org/10.5281/ZENODO.5571936\u003c/li\u003e\n\u003cli\u003eReiner, F.\u003cem\u003e et al.\u003c/em\u003e More than one quarter of Africa\u0026rsquo;s tree cover is found outside areas previously classified as forest. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2258 (2023). https://doi.org/10.1038/s41467-023-37880-4\u003c/li\u003e\n\u003cli\u003eReiner, F.\u003cem\u003e et al.\u003c/em\u003e (Zenodo, 2023).\u003c/li\u003e\n\u003cli\u003eRunning, S. W. \u0026amp; Zhao, M. Daily GPP and annual NPP (MOD17A2/A3) products NASA Earth Observing System MODIS land algorithm. \u003cem\u003eMOD17 User\u0026rsquo;s Guide\u003c/em\u003e \u003cstrong\u003e2015\u003c/strong\u003e (2015).\u003c/li\u003e\n\u003cli\u003eHigazi, A. \u0026amp; Abubakar Ali, S. Pastoralism and Security in West Africa and the Sahel: Towards Peaceful Coexistence. (2018).\u003c/li\u003e\n\u003cli\u003eXiao, N., Cai, S., Moritz, M., Garabed, R. \u0026amp; Pomeroy, L. W. Spatial and Temporal Characteristics of Pastoral Mobility in the Far North Region, Cameroon: Data Analysis and Modeling. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e0131697 (2015). https://doi.org/10.1371/journal.pone.0131697\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5860400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5860400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePastoralism is a major way of life in the Sahelian and Sudanian (SaSu) zone of Africa, playing an important social-environmental role through food production and the use of suitable land for seasonal migrations (transhumance). Using Earth Observation (EO) data, we systematically analyze environmental factors\u0026mdash;water access, soil properties, topography, vegetation cover, tree cover, road access, and biomass availability\u0026mdash; to assess the SaSu\u0026rsquo;s suitability for transhumance as well as for permanent farming systems, and provide perspectives on potential conflict zones between herders and farmers in case of conflicting interests. Our study is the first to present comprehensive and detailed transhumance corridors that account for environmental constraints. We show that 69% of conflicts from 2001\u0026ndash;2020 involve or are related to tensions between farmers and pastoralists, while 31% of conflicts are attributed to interactions between pastoralists. Our research provides valuable insights into the complex relationships between pastoralist communities and their socio-ecological environment and highlights the critical role of EO-based decision support systems in mapping and understanding pastoralism in the SaSu region.\u003c/p\u003e","manuscriptTitle":"Prospects for pastoralist-farmer conflict in Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-27 13:23:36","doi":"10.21203/rs.3.rs-5860400/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13ecbb74-4850-413d-a70b-83fec4ed16ae","owner":[],"postedDate":"February 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44707263,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":44707264,"name":"Scientific community and society/Social sciences/Interdisciplinary studies"},{"id":44707265,"name":"Scientific community and society/Agriculture"},{"id":44707266,"name":"Scientific community and society/Developing world"},{"id":44707267,"name":"Earth and environmental sciences/Climate sciences/Hydrology"}],"tags":[],"updatedAt":"2025-10-03T15:56:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-27 13:23:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5860400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5860400","identity":"rs-5860400","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}