Indicadores microbiológicos de qualidade do solo estão relacionados com maiores produtividades do cafeeiro no cerrado mineiro

Abstract

Microbiological indicators can be useful in predicting rates and direction of changes in soil quality. The evaluation of these indicators can guide farmers in their decision on how to manage their production systems, resulting in higher soil quality and, consequently, better crop production. Among these is the coffee production system, which has great economic and social importance in Brazil and in the world. Coffee is an important crop in the Minas Gerais state, the state with the largest production in the country. The objective of this work was to evaluate the relationship between physical, chemical and microbiological soil indicators with different coffee cultivars yield in the cerrado region of Minas Gerais, Brazil. The work was carried out with soil samples collected in the Alto Paranaíba mesorregion (18 ° 59 '26 "S and 46 ° 58' 9.5" W). The samples from the first study were collected in the dry and wet period, and were obtained from six plots with different productivities. Two cultivars of Catuai Vermelho IAC 99, two from Catuaí Vermelho IAC 144 and two from Rubi MG 1192, each cultivar with one plot of higher and lower productivity. In the second study, samples were collected from four plots with different yields, but from the same cultivar. The cultivars yields were obtained by the average of the 2014/2015, 2015/2016 and 2016/2017 harvests. The experimental procedure consisted of the collection of five composite samples in each plot, these were formed by grouping four sub-samples taken at a depth of 0-10 cm in the crown projection of the plant. The following microbiological attributes were evaluated: microbial biomass carbon, basal and induced respiration, metabolic quotient, activities of the enzymes urease, β-glucosidase, arilsulfatase and hydrolysis of fluorescein diacetate. The chemical and physical attributes were: texture, pH, organic matter and nutrients. The data were submitted to analysis of variance and, after validation of the statistical model, the averages were grouped by the Scott Knott algorithm at 5% of significance. Subsequently, the values of the physical, chemical and biological attributes were evaluated by Principal Component Analysis (PCA). The microbial indicators allowed to discriminate, mainly in the dry season, fields with different levels of productivity and were positively related to the higher productivities of the coffee plant. The climatic conditions influenced the responses and should be considered in the evaluation and elaboration of soil quality indexes. The physical-chemical attributes did not present the potential to discriminate different productivities and were much higher than the one required by the culture.