skip to main content
Idioma:

Fine scale mapping of phosphorus stocks in brazilian soils by geotechnologies: implications for a sustainable agriculture

Rosas, Jorge Tadeu Fim

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Escola Superior de Agricultura Luiz de Queiroz 2024-02-19

Acesso online

  • Título:
    Fine scale mapping of phosphorus stocks in brazilian soils by geotechnologies: implications for a sustainable agriculture
  • Autor: Rosas, Jorge Tadeu Fim
  • Orientador: Dematte, Jose Alexandre Melo
  • Assuntos: Aprendizagem De Máquina; Mapeamento Digital De Solos; Sensoriamento Remoto; Solos Tropicais; Digital Soil Mapping; Machine Learning; Remote Sensing; Tropical Soils
  • Descrição: Phosphorus, an essential nutrient for life and crucial for agriculture, is extracted from nonrenewable mineral reserves, raising concerns about sustainability. Therefore, mapping soil phosphorus stocks is vital for the efficient management of this resource and for the sustainability of the planet. However, mapping these stocks over large areas and at a high level of detail is not an easy task. Fortunately, new digital soil mapping (DSM) methodologies can contribute to obtaining increasingly accurate maps. The central hypothesis of this thesis is that the use of geotechnologies can contribute to the mapping of P stocks in Brazil, with high accuracy. This thesis was divided into two articles. The first aimed to map the main oxides of the soil clay fraction, which are closely related to P stocks. These maps were used as predictive covariates of P stocks in the second chapter, whose main objective was to map the stocks of total phosphorus (TP) and available phosphorus (AP) in Brazil. To map the main oxides of the clay fraction, we used a modeling dataset with 5,330 observations. Six spectral variables obtained from the Landsat historical series and seven terrain attributes derived from a digital elevation model were used to determine Fe2O3, Al2O3, and SiO2 using the Random Forest algorithm. The best predictions were observed for Fe2O3 in the superficial layer (RMSE = 47.0, RPIQ = 1.85, and R2 = 0.65), while the lowest predictions were for SiO2 in the underground layer (RMSE = 66.7, RPIQ = 1.55, and R2 = 0.19). The maps of the oxides in the 0-20 cm layer were used in predicting P stocks. In addition to these oxides, we included environmental covariates related to soil formation processes, such as relief, climate, and organisms, and other attributes, such as, for example, soil organic carbon and clay. We divided Brazil into two sub-regions, representing areas with native coverage and areas with anthropic coverage. From this, we built independent predictive models for each sub-region. In total, 28,572 samples for AP and 3,154 for TP were used in modeling. Our results showed that Brazil has a TP stock of 531 Mt and an AP stock of 17.4 Mt. The highest averages of TP stocks are in the Atlantic Forest biome (73.8 g/m²), which may be linked to the higher stocks of soil organic carbon in this biome. The highest average AP stocks are in the Caatinga biome (2.51 g/m²) for presenting younger soils and with low phosphorus adsorption capacity. We also found that the use of fertilizers significantly increased AP stocks, where agricultural areas always had higher AP stocks than native areas. The proposed approach was able to quantify Brazils\' P stocks with spatial distribution aligned with the understanding of Brazilian soils. In addition, it was possible to map the entire Brazilian territory for the first time with a scale of 30m.
  • DOI: 10.11606/T.11.2024.tde-09052024-072213
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Escola Superior de Agricultura Luiz de Queiroz
  • Data de criação/publicação: 2024-02-19
  • Formato: Adobe PDF
  • Idioma: Inglês
Links
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  8 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.