Overview: Soil quality's spatial variability is essential to spatially measure attributes that reflect the environmental state and the sustainability condition of agricultural systems. For that purpose, a methodology that investigates soil attributes (chemical, physical and biological) both spatially and in conjunction is necessary to obtain soil quality indices in agricultural systems. Considering the spatial distribution of observation points, it is assumed that they are dependant, which, in most cases, happens in studies involving soil science. The geostatistical analysis developed in the project incorporates the spatial relationships between the points to the georeferred data in the form of a correlation between the sampled points. It goes by the hypothesis that samples which are closer to one another in a specific area are more alike than samples separated by a greater distance. Three study areas are sampled, and each one houses a distinct agricultural system: soybean, in Campinas, SP; sugarcane, in Mogi Mirim, SP; and pasture (Brachiaria sp.), in Nova Odessa, SP.
Knowledge about the spatial distribution of soil quality indices makes it easier to study their relationship to the type of management, the effect on the environment, and the production capacity of agricultural systems. Methodological knowledge and the validation of the spatial distribution of soil quality indices and of their relationships within agro-energetic systems will aid both in optimizing the most adequate soil management system, by minimizing degradation effects on the environment, and in indicating management practices to make the soil more productive.
Objectives: To develop a methodology based in quantitative, multicriteria methods for obtaining soil quality indices (physical, chemical and biological) considering the spatial variability in agricultural production systems. Expected results: A scientific methodology developed by means of calculating soil quality indices and mapping soil quality indicators and agricultural systems' production.
TEAM
Leader: Célia Regina Grego – Researcher – Embrapa Satellite Monitoring
Institutions involved: Embrapa Monitoramento por Satélite, Embrapa Solos, Instituto Agronômico de Campinas (IAC), Instituto de Zootecnia (IZ), Centro de Energia Nuclear na Agricultura da Universidade de São Paulo (CENA/USP), Universidade Federal do Ceará, Universidade Federal de Juiz de Fora e Universidade Federal de São Carlos. |
