O que sinto? Um grande alívio? A sensação de dever cumprido? Felicidade por agora ter tempo pra descansar? Hum... Acho que nada disso! O que sinto mesmo é vontade de continuar trabalhando. Pena que o cenário acadêmico-científico não é muito animador atualmente.

Mas deixemos pra lá o fato de que não terei qualquer fonte de renda dentro de um mês...

## A defesa

A defesa de minha tese de doutorado está marcada para o dia 24 de fevereiro próximo, às 9 horas da manhã, na sala 24 do Curso de Pós-Graduação em Agronomia–Ciência do Solo da Universidade Federal Rural do Rio de Janeiro (UFRRJ). A banca examinadora será composta por Gustavo de Mattos Vasques (presidente), Wenceslau Geraldes Teixeira, Ronaldo Pereira de Oliveira e Waldir de Carvalho Júnior (suplente), todos da Embrapa Solos, Marcos Bacis Ceddia e Mauro Antônio Homem Antunes (suplente), ambos da UFRRJ, e Maria Leonor Ribeiro Casimiro Lopes Assad, da Universidade Federal de São Carlos.

## O resumo

Modern soil spatial modelling is based on statistical models to explore the empirical relationship among environmental conditions and soil properties. These models are a simplification of reality, and their outcome (soil map) will always be in error. What a soil map conveys is what we expect the soil to be, acknowledging that we are uncertain about it. The objective of this thesis is to evaluate important sources of uncertainty in spatial soil modelling, with emphasis on soil and covariate data. Case studies were developed using data from a catchment located in Southern Brazil. The soil spatial distribution in the study area is highly variable, being determined by the geology and geomorphology (coarse spatial scales), and by agricultural practices (fine spatial scales). Four topsoil properties were explored: clay content, organic carbon content, effective cation exchange capacity and bulk density. Five covariates, each with two levels of spatial detail, were used: area-class soil maps, digital elevation models, geologic maps, land use maps, and satellite images. These soil and covariate data constitute the Santa Maria dataset. Two packages for R were created in support of the case studies, the first (pedometrics) containing various functions for spatial exploratory data analysis and model calibration, the second (spsann) designed for the optimization of spatial samples using simulated annealing. The case studies illustrated that existing covariates are suitable for calibrating soil spatial models, and using more detailed covariates results in only a modest increase in the prediction accuracy that may not outweigh the extra costs. More efficient means of increasing prediction accuracy should be explored, such as obtaining more soil observations. For this end, one should use objective means for selecting observation locations to minimize the effects of psychological responses of soil modellers to conceptual and operational factors on the sampling design. This is because conceptual and operational difficulties encountered in the field determine how the motivation of soil modellers shifts between learning/verifying soil-landscape relationships and maximizing the number of observations and geographic coverage. For the sole purpose of spatial trend estimation, it should suffice to optimize spatial samples aiming only at reproducing the marginal distribution of the covariates. For the joint purpose of optimizing sample configurations for spatial trend and variogram estimation, and spatial interpolation, one can formulate a sound multi-objective optimization problem using robust versions of existing sampling algorithms. Overall, we have learned that a single, universal recipe for reducing our uncertainty in soil spatial modelling cannot be formulated. Deciding upon efficient ways of reducing our uncertainty requires, first, that we explore the full potential of existing soil and covariate data using sound spatial modelling techniques.