USE OF BIOCHAR IN THE COMPOSITION OF SUBSTRATES FOR THE PRODUCTION OF FOREST SEEDLINGS
organic waste, seedling production, charcoal
New technologies are being developed in the search to reduce production costs and to bring more sustainable alternatives, such as the use of organic residues from the agribusiness that have the potential for the formation of substrate, since it is possible to reuse the nutrients contained in these materials, in addition to promoting the reduction of the negative environmental impacts generated. Recently called biochar, the waste from the coal industry is a product rich in carbon, has physico-chemical characteristics capable of decreasing the leaching of nutrients, promoting greater water retention, contributing to the increase in porosity and sanding. Thus, the objective of the present work is to evaluate the effect of adding charcoal residues mixed to different materials in the formation of alternative substrate to produce quality seedlings of the species Tabebuia aurea, Pityrocarpa moniliformis e Handroanthus impetiginosus. The experiment will be conducted in a completely randomized design, consisting of eight treatments and four repetitions each. The mixture of the substrate will be constituted in the proportions of sand (25%), coconut fiber (50%), expanded vermiculite (25%) (control); sand (25%), coconut fiber (30%), expanded vermiculite (25%), biochar (20%) (T1); sand (25%), coconut fiber (20%), expanded vermiculite (25%), biochar (30%) (T2); sand (25%), , expanded vermiculite (25%), biochar (50%) (T3); Tropstrato® (100%) (T4); Tropstrato® (80%), biochar (20%) (T5); Tropstrato® (70%), biochar (30%) (T6); Tropstrato® (50%), biochar (50%) (T7). Sowing will be done manually, placing two seeds/containers. For seedling quality analysis, the neck diameter variables will be measured, number of leaves, length of the aerial part, dry root mass, dry mass of the aerial part and total dry mass. The analysis of the Dickson Quality Score to assist the interpretation of the data with the production of seedlings and the differences between treatments will be tested in a linear regression analysis using statistical software BioEstat®. This study aims to collaborate to expand the scientific knowledge of seedling production quality of forest species and the results obtained serve as indicative for models enrichment for the conservation of the species and recovery of degraded areas.