Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico

Abstract

The use of Remote Sensing (RS), especially in Precision Agriculture (PA) is in full expansion. Sensors embedded in the remotely piloted aircraft (RPA), complement the orbital and proximal RS, combined with the techniques of spatial statistical analysis and data modeling generate numerous information, which can assist in the evolution of agriculture. In a similar way, but with research carried out for a longer time, but not jointly, the use of biological control agents can fill the need to reduce the use of chemical inputs in crops, so that the activity approaches more a balance between production and sustainability, mainly in soybean (Glicine max (L.)), currently the main commodities produced in Brazil and in the world. This research presents a study on the effects of biological control on the vegetation indexes (VI) and on the of soybean yield components (YC). The research starts from the reality of conventional agriculture and seeks, through the use of biotechnology and disruptive technologies of Precision Agriculture, as is the case of RS through multispectral sensors embedded in RPA, as a way of non-destructive monitoring, to assist in making decision-making. The general objective of this research was to test biological control agents in the soybean culture, and to use vegetation indexes obtained with a multispectral sensor embedded in a RPA to identify the spatial variability produced by the use of these products and to estimate the yield of grains and their components. An experiment was implemented in the municipality of Tupanciretã, in the central region of RS, with treatments carried out during the sowing of a soybean crop through the application of biological control agents in the sowing furrow. Three treatments and a control were used (without application of the products). The first, using the fungus Trihcoderma harzianun, the second, using the same fungus mixed with the bacterium Bacillus amyloliquefaciens, and the third, with only that bacterium. The crop was imaged using RPA, of the fixed wing type, and an embedded multispectral sensor. Six images were taken, three in the vegetative stages of the crop (V4, V6 and V9) and three in the reproductive stages (R1, R2 and R6). From the images, five VI were generated, NDVI, NDRE, MPRI and SAVI, the latter with two soil adjustment constants (0.25 and 0.5). The data of the YC were obtained, with the measurement of crop characteristics, at the time of harvest, in which the vegetable samples were collected, in two planting lines x 0.80 m, making 120 sample units of 0.720 m2, 30 for each treatment and witness. From each sample, the number of plants, viable and non-viable pods (manually), the number of grains (electronic grain counter) were counted and, after determining the humidity, the dry matter masses of a thousand grains and total were measured, and their results were converted into m2. For statistical analysis of the data, descriptive statistics were used, which gave a general idea of the data, Pearson's Correlation analysis between the generated IV and the YC, analysis of variance or average rank, to compare the effect of treatments on the IV and YC, and regression analysis, to estimate productivity from the VI (with zonal statistic data). The results showed that the biological control agents applied in the experiment provided the treatments with a larger population of plants, when compared to the control, with possible vigor and health superior to the same, and statistically different values were detected in the IV, YC and productivity. The IV that best estimated productivity was NDRE, at the R1 stage, with a correlation of 0.718 and a coefficient of determination with productivity of 0.804. It is concluded that the best treatment was obtained with the mixture of T. harzianun and B. amyloliquefaciens (greater number of plants, viable pods, grains and greater productivity), than the multispectral sensor embedded in RPA, proved to be useful for monitoring the culture development in various phenological stages and that the best IV were NDRE, NDVI and SAVI, in the phenological stages R1 and R2.