Organic farm does not improve neither soil, or water quality in rural watersheds from southeastern Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Soil physical and microbiological attributes cultivated with the common bean under two management systems

Agricultural management systems can alter the physical and biological soil quality, interfering with crop development. The objective of this study was to evaluate the physical and microbiological attributes of a Red Latosol, and its relationship to the biometric parameters of the common bean (Phaseolus vulgaris), irrigated and grown under two management systems (conventional tillage and direct seeding), in Campinas in the state of Sao Paulo, Brazil. The experimental design was of randomised blocks, with a split-plot arrangement for the management system and soil depth, analysed during the 2006/7 and 2007/8 harvest seasons, with 4 replications. The soil physical and microbiological attributes were evaluated at depths of 0.00-0.05, 0.05-0.10, 0.10-0.20 and 0.20-0.40 m. The following were determined for the crop: density, number of pods per plant, number of beans per pod, thousand seed weight, total weight of the shoots and harvest index. Direct seeding resulted in a lower soil physical quality at a depth of 0.00-0.05 m compared to conventional tillage, while the opposite occurred at a depth of 0.05-0.10 m. The direct seeding showed higher soil biological quality, mainly indicated by the microbial biomass nitrogen, basal respiration and metabolic quotient. The biometric parameters in the bean were higher under the direct seeding compared to conventional tillage.

Phytogeographic retrospective in ecotonal areas guided by soil attributes

Re-establishing deforested ecosystems to pre-settlement vegetation is difficult, especially in ecotonal areas, due to lack of knowledge about the original physiognomy. Our objective was to use a soils database that included chemical and physical parameters to distinguish soil samples of forest from those of savannah sites in a municipality located in the southeastern Brazil region. Discriminant analysis (DA) was used to determine the original biome vegetation (forest or savannah) in ecotone regions that have been converted to pasture and are degraded. First, soils of pristine forest and savannah sites were tested, resulting in a reference database to compare to the degraded soils. Although the data presented, in general had a high level of similarity among the two biomes, some differences occurred that were sufficient for DA to distinguish the sites and classify the soil samples taken from grassy areas into forest or savannah. The soils from pastured areas presented quality worse than the soils of the pristine areas. Through DA analysis we observed that, from seven soil samples collected from grassy areas, five were most likely originally forest biome and two were savannah, ratified by a complementary cluster analysis carried out with the database of these samples. The model here proposed is pioneer. However, the users should keep in mind that using this technology, i.e., establishing a regional-level database of soil features, using soil samples collected both from pristine and degraded areas is critical for success of the project, especially because of the ecological and regional particularities of each biome.

OntoSDM: an approach to improve quality on spatial data mining algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

Soil greenhouse gas emissions and their relations to soil attributes in a sugarcane area

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)