SOIL PROPERTIES, CONDITION AND SOIL LOSSES FOR SOUTH AND EAST BRAZILIAN FOREST AREAS
TABLE OF CONTENTS
Page
GENERAL ABSTRACT................................................................................... i
RESUMO GERAL........................................................................................... ii
CHAPTER 1 .................................................................................................... 1
1 Introduction................................................................................................... 1
2 Literature Review.......................................................................................... 2
2.1 The universal soil loss equation (USLE) ..................................................... 2
3 Final Considerations...................................................................................... 5
4 Suggestions For Future Research................................................................... 6
4.1 The WEPP model ....................................................................................... 6
4.2 The GeoWEPP interface ............................................................................. 7
5 References..................................................................................................... 9
CHAPTER 2: Soil properties and hemc from soils cultivated with eucalyptus,
Brazil ............................................................................................................. 12
1 Abstract....................................................................................................... 12
2 Resumo ....................................................................................................... 13
3 Introduction................................................................................................. 14
4 Material and Methods.................................................................................. 18
4.1 The study area and soil description ........................................................... 18
4.2 Soil analyses............................................................................................. 19
4.2.1 High-energy moisture characteristic (HEMC)......................................... 20
5 Results and Discussion ................................................................................ 26
6 Conclusions................................................................................................. 41
7 References................................................................................................... 42
CHAPTER 3: Spatial distributed model for assessing soil erosion risk in a small
watershed ....................................................................................................... 47
1 Abstract....................................................................................................... 47
2 Resumo ....................................................................................................... 48
3 Introduction................................................................................................. 49
4 Material and Methods.................................................................................. 51
4.1 The study area and soil description ........................................................... 51
4.2 The universal soil loss equation (USLE) ................................................... 52
4.3 Soil loss tolerance (T)............................................................................... 58
4.4 Geographic information system (GIS)....................................................... 59
5 Results and Discussion ................................................................................ 60
5.1 USLE parameters...................................................................................... 60
5.2 Spatial distribution of soil loss .................................................................. 65
6 Conclusions................................................................................................. 71
7 References................................................................................................... 72
GENERAL ABSTRACT
AVANZI, Junior Cesar. Soil properties, condition and soil losses for south
and east Brazilian forest areas. 2009. 76 p. Dissertation (Doctorate in Soil
Science) – Federal University of Lavras, Lavras.1
Eucalyptus cultivation has increased in all Brazilian regions. In order to
recommend good management practices it is necessary to understand differences
in soil properties where eucalyptus is planted. In addition, aggregate stability
analyses have proved to be a useful tool to measure soil effects caused by
changes in management practices. Besides, the evaluation of soil erosion is an
important tool for planning of conservationist management actions allowing
appropriate changes on land-use and implementation of sustainable management
strategies in the long-term. Thus, the objectives of this study were: i) to
determine the main soil properties for different soil classes, and assess the
relationship between aggregate stability and changes in soils under eucalyptus
plantation, and ii) to predict the potential annual soil loss using the Universal
Soil Loss Equation (USLE) coupled in a Geographical Information System
(GIS). We studied representative soils within three eucalyptus cultivated
regions. In the Espírito Santo state the soils selected were classified as
dystrocohesive Yellow Argisol – PA1 (Hapludult), moderately rocky Yellow
Argisol – PA2 (Hapludult), and dystrophic Haplic Plinthosol – FX
(Phinthaquox). In the Rio Doce Valley, center-east region of Minas Gerais state,
the samples were collected in dystrophic Red-Yellow Latosol – LVA
(Haplustox) and dystrophic Red Latosol – LV (Haplustox). In the south region
of Brazil the area encompasses eutrophic Red Argisol – PVe (Rhodudalf),
dystrophic Red-Yellow Argisol – PVA (Hapludult), and dystrophic Haplic
Cambisol – CXbd (Dystrudept). Physical, chemical, and mineralogical analyses
were performed for the A horizon to characterize the predominant soil profiles.
Aggregate stability was measured using the high-energy moisture characteristic
(HEMC) technique. Aggregate stability ratio was greater than 50% for all soils.
This fact shows for highly weathered soils with large amount of 1:1 clay
minerals, that the aggregate stability index was high. In the Espírito Santo we
performed the USLE model in order to evaluate soil erosion. All the USLE
factors were generated in a distributed approach using GIS framework. Results
showed that the average soil loss was 6.2 t ha-1 yr-1. Relative to soil loss
tolerance, 86% of the area presented erosion rate smaller than the tolerable
value.
1
Guidance Committee: Marx Leandro Naves Silva – UFLA (Major Professor); L.
Darrell Norton – USDA-ARS-NSERL/Purdue University; Nilton Curi – UFLA.
i
RESUMO GERAL
AVANZI, Junior Cesar. Atributos do solo, ambientes e perdas de solo para
áreas florestadas no sul e leste do Brasil. 2009. 76 p. Tese (Doutorado em
Ciência do Solo). Universidade Federal de Lavras, Lavras.2
O cultivo de eucalipto tem aumentado em todas as regiões brasileiras.
Para recomendar práticas de manejo adequadas é necessário o entendimento dos
diferentes atributos do solo onde esta cultura está instalada. Além disso, a
análise da estabilidade de agregados tem provado ser uma boa ferramenta para
medir os efeitos causados no solo devido às mudanças nas práticas de manejo.
Além disso, a avaliação do processo erosivo é um importante instrumento no
planejamento do manejo conservacionista, permitindo realizar mudanças
apropriadas no uso do solo e programar estratégias de manejo em longo prazo.
Assim, os objetivos deste estudo foram: i) determinar os principais atributos do
solo para as diferentes classes de solo e avaliar sua relação com a estabilidade de
agregados em solos sob cultivo de eucalipto; e ii) estimar o potencial de perdas
de solo anual através da Equação Universal de Perdas de Solo (EUPS) acoplada
no Sistema de Informação Geográfica (SIG). Solos representativos de três
regiões cultivadas com eucaliptos foram utilizados. No Espírito Santo os solos
selecionados foram classificados como Argissolo Amarelo coesivo distrófico
(PA1), Argissolo Amarelo moderadamente rochoso (PA2) e Plintossolo Háplico
distrófico (FX). No Vale do Rio Doce, região centro-leste de Minas Gerais, as
amostras foram coletadas em um Latossolo Vermelho Amarelo distrófico (LVA)
e um Latossolo Vermelho distrófico (LV). Na região sul do Brasil a área abrange
um Argissolo Vermelho eutrófico (PVe), um Argissolo Vermelho Amarelo
distrófico (PVA) e um Cambissolo Háplico distrófico (CXbd). Análises físicas,
químicas e mineralógicas foram realizadas nos horizontes A dos perfis de solo
estudado. A estabilidade de agregados foi avaliada através da técnica highenergy
moisture characteristic (HEMC). A estabilidade de agregados foi maior
que 50% para todos os solos estudados. Este fato mostra que o índice de
estabilidade de agregados foi elevado para solos altamente intemperizados com
grandes quantidades de argilo-minerais 1:1. No Espírito Santo a avaliação do
risco de erosão foi realizada por meio da EUPS. Os fatores da EUPS foram
gerados de forma distribuídos utilizando a plataforma SIG. Os resultados
mostraram uma perda de solo média de 6,2 t ha-1 ano-1. Em relação à tolerância
de perdas de solo, 86% da área apresentaram taxas de erosão abaixo dos valores
de tolerância de perdas.
2
Comitê Orientador: Marx Leandro Naves Silva – UFLA (Orientador); L. Darrell
Norton – USDA-ARS-NSERL/Purdue University; Nilton Curi – UFLA.
ii
CHAPTER 1
1 INTRODUCTION
Erosion and sedimentation are naturally occurring processes. However,
human activities have accelerated these processes well beyond the rate allowed
by nature. The erosion processes include three mechanisms: detachment,
entrainment and transport of particles. For these processes be active the action of
water and/or wind is required. When these active elements stop their action, the
transported particles fall out on a surface. This process is called deposition. In
Brazil the most important active element is the water. In this context, the water
erosion can be considered one of the main problems linked to tropical soils
management, constituting significant causes of environmental degradation. It
may have an effect on both, the natural environment and the agricultural areas.
Advanced erosion processes not only decrease land productivity but can also
generate transport of nutrients, organic matter and agrochemical products that
can contaminate and fill up water bodies.
The knowledge about soil erosion process as well as how fast soil is
eroded is helpful in the planning of conservation management actions. Modeling
can provide a quantitative and consistent approach to predict soil erosion and
sediment delivery ratio under a wide range of conditions (Bhattarai & Dutta,
2007). In addition, it can be used to test hypotheses and to predict both the
appropriate soil management and land use for each site (Beven, 1989; Grayson
et al., 1992; Tucci, 1998).
The models can be defined as simplified approaches of the natural
ecosystem (Batchelor, 1994) which try to better understand the essential aspect
from a phenomenon. The models can be divided into empirical and physically-
1
based models. Empirical model is a simple representation of a system or
phenomenon that is based on measurements and/or observations. It usually
establishes relationships between the variables. Examples of empirical-based
models used for soil loss evaluation are the Universal Soil Loss Equation
(USLE) (Wischmeier & Smith, 1978), the Modified Universal Soil Loss
Equation (MUSLE) (Williams, 1975), and the Revised Universal Soil Loss
Equation (RUSLE) (Renard et al., 1997). On the other hand, physical-based
model uses physical variables that can describe a behavior with details of a
physical phenomenon. Thus, it can be updated in real time, presenting a great
improvement due to the fact that it can be extrapolated to other sites (Tucci,
1998). However, a big data-set should be constructed in order to calibrate the
relatively large number of parameters. To do so, physically-based models have
been used such as the Water Erosion Prediction Project (WEPP) (Flanagan &
Nearing 1995), the Limburg Soil Erosion Model (LISEM) (De Roo et al., 1996),
the European Soil Erosion Model (EUROSEM) (Morgan et al., 1998), the
Geospatial interface for the Water Erosion Prediction Project (GeoWEPP)
(Renschler, 2003), and the Soil and Water Assessment Tool (SWAT) (Gassman
et al., 2007), among others.
2 LITERATURE REVIEW
2.1 The Universal Soil Loss Equation (USLE)
The most known, applied, and implemented approach for estimating
long-term average annual soil loss is the Universal Soil Loss Equation (USLE)
developed by Wischmeier & Smith (1978). It is a simple empirical equation
based on factors representing the main processes causing soil erosion. It was
developed as a conservation planning tool, and, in recent times it has become the
2
Soil Conservation Service’s primary tool for enforcing conservation policy. For
developing such model it were used data from 49 U.S. locations representing
over 10,000 plots including measurements of runoff and soil erosion, which
were compiled and studied at Purdue University (Wischmeier & Smith, 1978).
Most of this data was collected between 1930 and 1950, and, the collection
continued to grow into the late sixties. Using this information and the results
from previous empirical studies of erosion, Wischmeier & Smith (1965)
developed the following equation that estimates average annual soil loss using
rainfall, soil, topographic, and management data:
A R K L S C P Eq. 1
where A is the computed long-term average annual soil loss per unit area, R is
the rainfall and runoff factor, K is the soil erodibility factor, LS is the
topographic factor, C is the cover and management factor, and P is the support
practice factor. Each of these factors is designed to account for critical processes
that can affect the soil loss on a given slope.
The rainfall and runoff factor (R) is designed to quantify the raindrop
direct impact effect and provide relative information on the amount and rate of
runoff likely to be associated with the rain. It represents the potential erosivity
presented in the rainfall and runoff at the particular location and is defined
empirically as a function of the total storm energy and the maximum 30-minute
intensity. The soil erodibility factor (K) is used to represent the differences in the
natural susceptibilities of soils to erosion. The slope length (L) and slope
steepness (S) factors represent the topography of the terrain. They are designed
to account for topographic factors which can affect the rate of energy
dissipation. The C factor is the ratio of soil loss from land cropped under
specific conditions to the corresponding loss from tilled, continuous fallow
conditions. The correspondence of periods of highly erosive rainfall with periods
of poor or good plant cover differs appreciably between climatic areas;
3
therefore, the value of C for a particular cropping and management system will
not be the same for all parts of the world. Locally, the C values are derived using
specific rainstorm-timing probabilities and research data that reflect the erosion
reducing effectiveness of crops and management during successive periods
within a rotation cycle (Wischmeier, 1972). The support practice factor, P, is
similar to C, except that it is intended to account for additional effects such as
contour farming, terraces, and strip cropping. By definition, P is the ratio of soil
loss with a specific support practice to the corresponding loss with conventional
up-and-down slope tillage.
The approach for determining these factors is based on the concept of
the unit plot. This is a slope 20.1 m long with 9% slope, left fallow with regular
up and down tillage. Using this standard condition (where LS, C and P all equal
to one) and calculated values of R, measurements of the soil loss can be used to
determine the value of K. From this baseline, the other factors can be determined
by measuring the soil loss on plots where one of the factors is changed and the
corresponding change in soil loss is evaluated against that under the standard
conditions. The drawback of this model is that it is not capable of simulating
deposition, sediment yield, channel erosion, and gully erosion. In addition, such
equation makes no differentiation between rill and interrill erosion, predicting
their combined effects. Despite the aforementioned limiting aspects, the USLE
has presented consistent results when coupled with Geographical Information
System (GIS) for estimating the magnitude and spatial distribution of soil loss.
4
3 FINAL CONSIDERATIONS
Models are essential tools to assess the erosion process. They can be
used to simplify reality or to analyze a system to be constructed (diverse
scenarios). Modeling provides support in decision making and can give us more
security to recommend a specific management practice, specially, when
modeling in a distributed approach, which shows a global view about what is
happening into the watershed. In erosion studies there are empirical and physical
based models, both with advantages and disadvantages. However, these models
do not compete with each other, but they can be treated as complementary
models because they are applied in different situations. While empirical-based
model is simple and needs few parameters, physical-based model is more
complex and needs more parameters. Thus, the adoption of a particular model
depends on the amount of available data for a give region.
5
4 SUGGESTIONS FOR FUTURE RESEARCH
4.1 The WEPP Model
The WEPP hillslope model is a physically-based continuous simulation
model (Flanagan & Nearing, 1995). It uses fundamental hydrological and
erosion mechanics as opposed to the empirically-based USLE model. It is based
on a two-dimensional hillslope profile approach and it is able to predict
deposition and erosion along the soil profile as well as sediment delivery from
the profile.
The WEPP hillslope model does not have a slope length and slope
gradient factor as in USLE. Instead, the slope gradient and length inputs of
WEPP are deeply integrated into the hydrological and erosion components of the
model. This means that the measurements of slope length and slope gradient are
not limited to affect L and S factors as in USLE, but instead they affect the
calculations of runoff, friction, transport capacity and various other factors
(Flanagan & Nearing, 1995).
The slope length of the WEPP profile is defined as the distance from the
top of the hillslope to the end of the hillslope (usually ending in a channel or
impoundment). Selection of the length and slope profile in WEPP is thus easier
than in USLE in the case of a single hillslope. Since WEPP is able to calculate
both detachment and deposition along the hillslope profile, it is therefore
important that an accurate representation of the slope profile (length and
gradients) be used.
Applications of the WEPP models include all those of the USLE as well
as many additional applications beyond the scope of USLE. According to Lane
et al. (1992) some of the applications include: i) location of sediment
detachment on a slope, either for individual storms or for long-time averages; ii)
evaluation of complete land treatment, including waterways, terraces, tillage
6