Banca de DEFESA: CAUAN FERREIRA ARAUJO

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : CAUAN FERREIRA ARAUJO
DATE: 10/11/2022
TIME: 09:00
LOCAL: Remoto
TITLE:

Soil mapping using multiscalar analysis for application to small-scale agriculture

KEY WORDS:

Keyword: digital soil mapping, geomorphometry, pedometry, scale.

PAGES: 76
BIG AREA: Ciências Agrárias
AREA: Agronomia
SUBÁREA: Ciência do Solo
SPECIALTY: Gênese, Morfologia e Classificação dos Solos
SUMMARY:

Soil information is essential for the sustainable intensification of family production systems, and it is recommended that soil classes be mapped down to the subgroup level, in addition to the mapping of textural classes. In the Amazon context, soil information needs to be on a scale compatible with small rural properties, despite the large extension of the region, so that it can be useful for family farmers and technical assistance. To this end, it is necessary to adopt multiscale strategies for digital soil mapping. In the present study, the mapping of textural classes was performed on a scale of 1:25,000 and the mapping of soil classes on a scale of 1:100,000. On the other hand, the multiscale influence of topography on soil distribution has a complex pattern that is related to the overlapping of pedological processes that occurred at different times and driving forces that are correlated with different scales. In this sense, we tested the hypothesis that generalized geomorphometric covariates at multiple scales can improve the accuracy of pedometric modeling. In the mapping of textural classes, the Random Forest algorithm was applied in a multiscale geomorphometric database to predict the granulometric percentages on the soil surface. The generalized covariates improved the accuracy of the textural classification, with the Kappa Index increasing from 0.43 to 0.62. In the mapping of soil classes, fuzzy set models were applied using generalized covariates to define preferential environments for the occurrence of the pedogenetic processes of gleization, elutriation and clay translocation, as opposed to the wide spatio-temporal distribution of the ferralization process. Fuzzy membership rules and covariate scales were defined for each of the processes, considering the soil knowledge obtained from soil surveys in transects. The generalized covariates improved the accuracy of the mapping, with the Kappa Index going from 0.4 to 0.69. The results demonstrate that knowledge-based models that observe the multiscale influence of topography on soil formation processes can improve the prediction of soil classes in intermediate-level soil surveys. Therefore, it is concluded that the use of generalized geomorphometric covariates in multiple scales results in greater accuracy of the models, being a flexible method for use both in machine learning approaches and in modeling based on the pedologist's knowledge. Still, from the geomorphometric point of view, it is necessary to test generalization methods, focusing on the preservation of features relevant to the soil-landscape relationship.

COMMITTEE MEMBERS:
Presidente - 1776710 - TROY PATRICK BELDINI
Interna - ***.720.282-** - LUCIETA GUERREIRO MARTORANO - EMBRAPA
Interno - 1776327 - THIAGO ALMEIDA VIEIRA
Externo ao Programa - ***.767.905-** - JOSE AUGUSTO AMORIM SILVA DO SACRAMENTO - IFBA
Externo ao Programa - ***.087.702-** - RAIMUNDO COSME DE OLIVEIRA JUNIOR - EMBRAPA
Externa ao Programa - 2426826 - CELESTE QUEIROZ ROSSI - UFOPAExterna à Instituição - MICHELE DUARTE DE MENEZES - UFLA