| Resumo: |
ABSTRACT: Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to use other analysis techniques such as artificial neural networks and regression trees. Thus, the objective of the study was to estimate the field capacity (FC), permanent wilting point (PWP), and available water (AW) in soils of Santa Catarina (SC), through multiple linear regressions (MLR), artificial neural networks (ANN), and regression trees (RT), more efficiently than the current pedotransfer functions. For this, samples of the horizons A and B of 70 profiles were collected to determine the texture, plasticity limit, FC, PWP, AW, specific surface (SS), organic carbon (OC) content, and microporosity. Pedotransfer functions were generated through MRL, ANN, and RT, considering as dependent variables the FC, PWP, and AW, and as independent variables the content of clay, silt, OC, plasticity limit, SS, and microporosity, through the test of four models, for surface and subsurface horizons. The RT estimated FC, PWP, and AW better than ANN and MRL. The best models to estimate water retention were those that used microporosity. When the database has few input variables, the model with clay, silt, and OC content is an alternative to estimate FC, PWP, and AW.
|
