Goat production is an important economic activity for rural communities in the Gran Chaco of Argentina. Goat corrals are important for the survival of peridomestic populations of Triatoma infestans. This study evaluated the impact of modifying the traditional structure of goat corrals on T. infestans populations and goat productivity in the region of Los Llanos (La Rioja). Thirty-nine experimental corrals were constructed and 57 traditional corrals were used as controls. We evaluated the infestations of the control and experimental corrals for five years following construction of the structures. The results showed that the new structures did not prevent the colonization, although it enhanced the detection of infestation at low densities of T. infestans. No significant difference was found in T. infestans population abundance between control and experimental corrals, probably because of the different detectability in the two types of structures, especially among the small nymphs. Although goat productivity average was higher in experimental than in control corrals, no significant difference was found because of high variability. The new structures can be used as a complement to promote the development of rural communities. Acceptability and adoption of the new corrals by the owners was high, as the enclosures offered better protection for the goats, increased growth of kids and facilitated herd handling.

We model potential distribution for three species of rodents known to be reservoirs of zoonotic diseases: Calomys musculinus, Oligoryzomys flavescens and O. longicaudatus. These models provide general distribution hypotheses obtained using environmental data from record localities. Satellite remote sensing is then used to extrapolate climatic and ecological features of potentially suitable habitats for these rodents. In the three species mapped, we found high overall correspondence between predicted (based on environmental data) and specimen based distributions. The maps proposed here provide several advantages over dot and shaded outline maps. First, the predictive maps incorporate geographically explicit predictions of potential distribution into the test. Second, the validity of the predictive map can be appreciated when localities of previous records of the studied species, not used as training sites or used as control sites, are overlaid on the map. In this approach, environmental factors, criteria and analytical techniques are explicit and can be easily verified. Hence, we can temporally fit data in more precise distribution maps.