Small ruminant farming, despite its nutritional and economic benefits, faces significant challenges, particularly regarding animal health. Peste des Petits Ruminants (PPR), a viral disease caused by a Morbillivirus, frequently infects herds, highlighting the need for a better understanding of its spatial distribution, risk areas, and control strategies. A study was conducted on 210 farms in three agro-ecological zones of South Kivu province (Mwenga, Uvira, and Kalehe), with 70 farms surveyed per site. Data collected focused on production systems and disease-related information. Data from the field were processed using Microsoft Excel and analyzed with XLSTAT software. The Pearson chi-square test was employed to determine the association of potential factors with PPR seropositivity, while univariate and multivariate logistic regression analyses were used to explore the relationship between PPR seroprevalence and risk factors at two sites (Mwenga and Kalehe). The Uvira site lacked sufficient serological data. Using the Maxent model, the study predicted the potential distribution of PPR based on environmental data and identified two types of niches: the fundamental niche, where species can theoretically exist, and the realized niche, which reflects real-world conditions influenced by environmental interactions. Key results revealed that farm characteristics such as water source, watering method, rearing system, species type, sex, age, and cleaning frequency significantly influenced PPR seroprevalence. Risk factors for infection included animal sex (OR = 91.73; CI = 21.15 - 39.60). This very high odds ratio reflects the fact that females have a longer life cycle than males, agro-ecological zone (OR = 8.28; CI = 4.75 - 14.42), and farming system (OR = 0.42; CI = 0.19 - 0.905). The highest prevalence (31.11%) was found in the high-altitude agro-ecological zone, and animals in the agropastoral system were most susceptible to infection (31.85%). Mapping of the study sites revealed three types of grazing lands based on infection risk. Uvira and Kalehe territories had more high-risk pastures than Mwenga. The study concluded that the MaxEnt model, incorporating Euclidean space and livestock farming systems as factors, is useful for controlling PPR in South Kivu and regions with similar farming and pasture conditions.
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