Espectrorradiometria difusa de solos e sua aplicabilidade na agricultura

• Ano de defesa: 2018
• Autor(a) principal: Silva, Daniely Vaz Rodrigues
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Tecnologia em Agricultura de Precisão; UFSM; Programa de Pós-Graduação em Agricultura de Precisão; Colégio Politécnico da UFSM
• Palavras-chave em Português: Agricultura de precisão; Espectrorradiometria de solos; Geoestatística; Regressão linear múltipla; Precision agriculture; Soil spectroradiometry; Geostatistics; Multiple linear regression; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/20733

This dissertation presents a study of soil spectroradiometry. The need to develop new technologies and new equipment to obtain more accurate data that can be correlated with other properties difficult to obtain is constant, in order to reduce costs and environmental impact. State-of-the-art tecnology and sustainable agricultural production. In this perspective, the improvement of the geotechnologies in Precision Agriculture has been collaborating along with other areas of knowledge with agroeconomics, from the identification of the characteristics and essential variants of the agricultural production that optimize and potentiate the applied resources. Specifically, soil spectroradiometry is used to obtain the data by reflectance, the measurement is made by the comparison between the reflected radiation flux and the amount of radiation incident on the ground. Based on these premises, this research aims to analyze the relationship between the data obtained by means of spectroradiometry, with the aid of statistics and geostatistics, whose quantitative or qualitative observations will be used to infer the properties of the spatial phenomenon under study. Thus, the dissertation was divided into two articles. The first objective was to evaluate the spectral data of several soil samples to establish correlations with chemical attributes. Multiple regression equations were obtained using the statistical software IBM SPSS (Statistics version 12) and R (R CORE TEAM, 2015), which made it possible to quantify these attributes. And the second article was to use regression equations previously predicted to evaluate the possibility of estimating clay content in soil samples from its energy reflected in a Precision Vitiniculture area. The results demonstrated the great performance for the generation of the prediction equations, reaching adequate R² in these equations with the multiple linear regression procedure. However, for the application of the estimation in the area it is necessary to take into account the factors that interfere in the reflectance, necessitating further studies mainly regarding the influence of mineralogy on soil reflectance.