Multiple traps compromise the sustainability of most livestock farmers in Sierras del Este, Uruguay.

Livestock system structure is a relevant factor in determining the decision-making processes and the initiatives to reduce the vulnerability to external drivers. The definition of livestock stocking density (LSD) is a key factor as it determines the sustainability of the activity. LSD adjusted to grassland productivity in space, and time has been advocated by researchers and policy makers. However, most livestock farmers fail to adjust the LSD, and therefore, their vulnerability increases. This study explored the hypothesis that productive decisions in livestock systems include information that reinforced the vulnerability. The main objective of this study was to evaluate vulnerability of livestock farmers through the information that guides the decision-making process. We analyze information based on which farmers define the LSD, focusing on the relationship between the farmers' perception of grassland productivity, the main reasons for their decisions, and grassland productivity using remote sensing techniques. The research strategy integrated interviews with farmers regarding LSD and grassland aboveground net primary productivity estimates. The results showed that 77% of the farmers use LSD higher than the carrying capacity, although 66% of them do so knowingly. Consequently, most farmers are in a situation of high vulnerability, especially to rainfall regime variability. Almost all farmers are affected by at least one trap: poverty, rigidity, or gilded. A clear contradiction is identified between the farmer's decisions and the current recommendations suggested by researchers and public policy, which are mainly explained by the farmers' optimism. Results highlight the access to information as a great barrier to the adoption of adaptive strategies. We show for the first time the main traps and its causal associated mechanisms, which limit the decision-making of Uruguayan livestock farmers. The proposed framework can be used in other agricultural systems to identify barriers and traps, as the first step toward increasing agroecosystem sustainability. [ABSTRACT FROM AUTHOR]