Levantamento e identificação de nematoides em seringais no Triângulo Mineiro, e sua relação com NDVI

Abstract

The global demand for natural rubber is remarkable, along with the fact that it contains polymers that have not yet been artificially synthesized, hundreds of products are manufactured exclusively using this material, due to its characteristics of elasticity, wear resistance, permeability, insulating capacity, among other dynamic properties, turning the rubber tree - the main source of natural rubber in the world - to be a strategic crop. Originally from the Amazon rainforest in Brazil, it was spread worldwide by the British. currently in the countries where it is cultivated, the rubber tree plays a primary role in their economy. Alike all crops, the rubber tree is affected by many important pathogens, which can reduce yield and even cause plant death. Among these, the pathogen Meloidogyne exigua race 3, found associated with the rubber tree in its place of natural occurrence, has been recognized as an important pathogen for the crop in the states out of the Amazon rainforest, considered as the 'escape area' for Microcyclus ulei, the main pathogen limiting rubber cultivation in the Amazon Forest region. This study evaluated the infestation of rubber tree forests in the Triângulo Mineiro region, and their relation to the seedling’s origin; and detect this pathogen using remote sensing. An infestation percentage of 20% of the visited areas was found, at high populations. Furthermore, Pratylenchus spp. and Aphelenchoides spp. were also detected at high frequency and high populations in the samples. Regarding the origin of the seedlings, it was observed that the pathogen was introduced in Triângulo Mineiro via contaminated seedlings produced in areas where it had already been reported, such as the State of Goiás, where locally established nurseries with infected seedlings grew and disseminated the pathogen in the region. It was possible to distinguish infested areas from areas free of M. exigua with remote sensing using the NDVI vegetation index, expressing statistically significant values.