Uso de aeronave remotamente pilotada (drone) para estudo do comportamento de búfalos em sistema de produção no Amapá, Amazônia, Brasil.

Abstract

This study evaluated the feasibility of using drones to monitor the behavior of Murrah buffaloes (Bubalus bubalis) in two distinct production systems (extensive and intensive) in the State of Amapá, Brazil. The objective was to test different altitudes (20m, 15m, 10m, 5m, 3m, and 2m) of the DJI Mini 2 Fly More drone, aiming to identify the ideal height to minimize escapes, stress, and negative behavioral responses. A height of 15m was selected for the construction of Ethograms, allowing for the safe and non-invasive identification of the buffaloes' behavioral repertoires. The data distribution was analyzed for normality using the Shapiro-Wilk test (W = 0.803; p < 0.05) and homoscedasticity (F= 0.345; p = 0.558), and was found to be homogeneous. Animal reactivity was assessed using Spearman’s correlation, and the effect of the environment on response variables was analyzed by Multivariate Permutational Analysis of Variance (PERMANOVA). Principal Coordinate Analysis (PCoA) was used to explore the spatial distribution of the data. With 104 hours of stored images, 17 types of behaviors were identified, grouped into 6 categories. The results validated the protocol for using the DJI Mini 2 drone for minimally invasive aerial monitoring, providing a better understanding of buffalo behaviors. The buffaloes' reaction to the drone decreased with increasing altitude, with 15 meters being the most suitable height for monitoring, as it minimizes stress and behavioral changes. Confined buffaloes (Area 2) showed greater reactivity, spending more time in alert and tense states, compared to buffaloes on pasture (Area 1), which showed less reactivity due to greater freedom and environmental stimuli. Multivariate analysis and PERMANOVA confirmed significant differences between the areas, and the Ethograms revealed greater behavioral diversity in Area 1 (12 types of behaviors) compared to Area 2 (8 types).