Carlos Gustavo Tornquist

Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil

Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.

Uso de regressões logísticas múltiplas para mapeamento digital de solos no Planalto Médio do RS

Logistic nominal regressions establish mathematical relations between continuous or discrete independent variables and discrete dependent variables. The prediction potential of the occurrence and distribution of soil classes in the region Ibiruba and Quinze de Novembro, RS, Brazil was evaluated. Using a digital elevation model (DEM) with 90 m resolution, were calculated several topographic characteristics (elevation, slope, and curvature) and hydrographic variables (distance to rivers, flow length, topographical wetness index, and stream power index). Multiple logistic regressions were established between the soil classes mapped on the basis of a traditional survey at a scale of 1:80.000 and the land variables calculated using the DEM. The regressions were used to calculate the probability of occurrence of each soil class. The final estimated soil map was drawn by assigning the soil class with highest probability of occurrence to each cell. The general accuracy was evaluated at 58 % and the Kappa coefficient at 38 % in a comparison of the original soil map with the map estimated at the original scale. A legend simplification had little effect to increase the general accuracy of the map (general accuracy of 61 % and Kappa coefficient of 39 %). It was concluded that multiple logistic regressions have a predictive potential as tool of supervised soil mapping.

Digital Soil Mapping Using Logistic Regression on Terrain Parameters for Several Ecological Regions in Southern Brazil

As the relationship between soils and landscape within the context of soil formation is well known, predictive relationships between soils and soil formation factors can be established by regression techniques, relating soil and terrain attributes to occurrence of soil classes. This study proposes the production of maps using logistic regression on soil and terrain information from a pilot area to reproduce the original map and predict soil distribution in other similar landscapes in three study areas (Ibibuba Municipality, Sentinela do Sul Municipality, and Arroio Portao Watershed) in map scales from 1:30,000 to 1:50,000 and located in three ecological regions in Southern Brazil (Planalto, Encosta da Serra do Sudeste, and Depressao Central, respectively). By using logistic regressions for digital soil mapping, the method predicts the occurrence of soil units based on reference soil maps (produced by conventional methods), and on several parameters derived from a USGS SDTS-SRTM DEM, namely slope gradient, profile curvature, planar curvature, curvature, flow direction, flow accumulation, flow length, Stream Power Index (SPI), and Topographic Wetness Index (TWI). Results show that parameters such as elevation, curvature, SPI, TWI, and distance to streams are more frequently selected as parameters for predicting the occurrence of soil classes, with overall percent correct from 61% to 71%, and Kappa Index from 36% to 54% when the maps produced are compared with the original soil maps with a simplified legend (which simulate the production of soil maps with smaller scales that the original soil map). The prediction of soil map units using logistic regressions generated reliable soil maps, and the method appears to deserve more research effort, given the reliability and low cost of the resulting information.

Long-term C-CO2 emissions and carbon crop residue mineralization in an oxisol under different tillage and crop rotation systems

Soil C-CO2 emissions are sensitive indicators of management system impacts on soil organic matter (SOM). The main soil C-CO2 sources at the soil-plant interface are the decomposition of crop residues, SOM turnover, and respiration of roots and soil biota. The objectives of this study were to evaluate the impacts of tillage and cropping systems on long-term soil C-CO2 emissions and their relationship with carbon (C) mineralization of crop residues. A long-term experiment was conducted in a Red Oxisol in Cruz Alta, RS, Brazil, with subtropical climate Cfa (Koppen classification), mean annual precipitation of 1,774 mm and mean annual temperature of 19.2 o C. Treatments consisted of two tillage systems: (a) conventional tillage (CT) and (b) no tillage (NT) in combination with three cropping systems: (a) R0- monoculture system (soybean/wheat), (b) R1- winter crop rotation (soybean/wheat/soybean/black oat), and (c) R2- intensive crop rotation (soybean/ black oat/soybean/black oat + common vetch/maize/oilseed radish/wheat). The soil C-CO2 efflux was measured every 14 days for two years (48 measurements), by trapping the CO2 in an alkaline solution. The soil gravimetric moisture in the 0–0.05 m layer was determined concomitantly with the C-CO2 efflux measurements. The crop residue C mineralization was evaluated with the mesh-bag method, with sampling 14, 28, 56, 84, 112, and 140 days after the beginning of the evaluation period for C measurements. Four C conservation indexes were used to assess the relation between C-CO2 efflux and soil C stock and its compartments. The crop

The southern Brazilian grassland biome: soil carbon stocks, fluxes of greenhouse gases and some options for mitigation

The southern Brazilian grassland biome contains highly diverse natural ecosystems that have been used for centuries for grazing livestock and that also provide other important environmental services. Here we outline the main factors controlling ecosystem processes, review and discuss the available data on soil carbon stocks and greenhouse gases emissions from soils, and suggest opportunities for mitigation of climatic change. The research on carbon and greenhouse gases emissions in these ecosystems is recent and the results are still fragmented. The available data indicate that the southern Brazilian natural grassland ecosystems under adequate management contain important stocks of organic carbon in the soil, and therefore their conservation is relevant for the mitigation of climate change. Furthermore, these ecosystems show a great and rapid loss of soil organic carbon when converted to crops based on conventional tillage practices. However, in the already converted areas there is potential to mitigate greenhouse gas emissions by using cropping systems based on no soil tillage and cover-crops, and the effect is mainly related to the potential of these crop systems to accumulate soil organic carbon in the soil at rates that surpass the increased soil nitrous oxide emissions. Further modelling with these results associated with geographic information systems could generate regional estimates of carbon balance.

Simulação da dinâmica do carbono do solo numa microbacia rural pelo modelo Century

The objective of this study was to combine the Century model with geoprocessing and remote sensing techniques, in order to evaluate spatial-temporal dynamics in total organic carbon (TOC). The study was carried out in a small rural watershed in Northeastern of Rio Grande do Sul State, Brazil. The area was initially under forest cover, which was progressively converted to agriculture in a period of 80 years. The proper calibrating of the model, especially in terms of TOC decomposition parameters, during the initial years of tillage, was essential to produce a coherent simulation of TOC values. Regarding the spatial distribution of TOC, it was observed that greatest impact of soil management on original contents occurred in the areas under cultivation since 1925, which showed a decline of 58.2% in TOC. The Century model integration with a geographic information system makes it possible the study of spatial-temporal dynamics of TOC and the performing of TOC distribution inventories.

Soils and Surface Waters as Affected by Long-Term Swine Slurry Application in Oxisols of Southern Brazil

Abstract The accelerated expansion of swine production in Brazil has increased the generation of liquid wastes, which are usually applied to agricultural soils after a simplified treatment and pose potential environmental impacts. The objective of this study was to assess the agronomic and environmental impacts of long-term application of swine slurry (SS) on soil and stream water properties in watersheds dominated by Oxisols in Quinze de Novembro of Southern Brazil. Soil samples (0–30 cm) were collected from farms with continuous application of SS since 1990 at low (40–80 m 3 SS ha −1 year −1 ) and high (120–300 m 3 SS ha −1 year −1 ) rates of SS. Surface water samples were collected from streams adjacent to the farm fields. Long-term SS application did not change total organic C and particulate organic C compared to cropland and woodland soils without SS application. The high rates of SS increased total N, P, Cu, and Zn and available P and Cu in the topsoil (0–10 cm) compared to woodland and cropland soils without SS application. Surface water analyses showed that fecal coliform bacteria and biological oxygen demand exceeded the legal limits for high quality water (Class 1). Other water parameters (such as NO − 3 , phosphate and total suspended solids) were within the acceptable limits. Long-term disposal of SS in cropland under no-tillage had impaired water quality in Quinze de Novembro, especially biological parameters. Some best management practices should be adopted, including more rigorous control of SS application to cropland as well as requiring edge-of-field and riparian vegetative buffers.

Avaliação de cinco algoritmos de árvores de decisão e três tipos de modelos digitais de elevação para mapeamento digital de solos a nível semidetalhado na Bacia do Lageado Grande, RS, Brasil

O mapeamento digital de solos (MDS) tem como base a geracao de sistemas de informacoes que permitem estabelecer relacoes matematicas entre variaveis ambientais e solos e, dessa forma, predizer a distribuicao espacial das classes ou propriedades dos solos. Dentre as abordagens mais utilizadas, as arvores de decisao tem se destacado por apresentar bons resultados no MDS. Por outro lado, dada a disponibilidade de novas fontes de informacao sobre a elevacao, torna-se necessario o teste e avaliacao de modelos digitais de elevacao (MDE) quanto ao seu uso para o MDS. Este estudo testa cinco algoritmos de arvores de decisao (Simple Chart, Random Tree, REP Tree, BF Tree e J48) e tres MDE (Aster GDEM, SRTM e SRTM V3) para o MDS a nivel semidetalhado, em situacoes em que o principal fator diferenciador entre os tipos de solo e o relevo. O uso do MDE Aster GDEM e arvore de decisao com algoritmo J48, Simple Tree e BF Tree foram os que produziram modelos de arvore de decisao capazes de produzir mapas de solo com maior similaridade ao mapa de referencia.

Evolução do uso do solo em uma área piloto da região de Vacaria, RS

The grasslands of Campos de Cima da Serra region, located in NE Rio Grande do Sul, were originally occupied by extensive cattle ranching. However, in the 1950s, a transition took place in land use from native vegetation to modern cash crop agriculture, which comprised mainly annual grain crops and apple orchards. This study applied remote sensing techniques for spatial and temporal detection of human-induced land use changes in this 50-years period to a pilot area, including part of the urban area of Vacaria and agricultural land to the north of this city. The earliest available aerial photos, from 1964, allowed assessment of initial shifts in land use. Subsequent temporal analyses relied on Landsat imagery from the 70s, 80s and 90s, and a 2005 EROS-A high resolution image. These analyses produced land use maps, which were overlayed, resulting in a land use evolution map from the beginning of agriculture up to the present day. Approximately 40% of the original grasslands were converted to annual grain crops. Conversion rates of grasslands into other uses were about 1.4 km2 year-1 in the 1960-1995 period, and 0.7 km2 year-1 from 1996 to 2005. On the other hand, areas of forests and riparian forests have not changed significantly throughout the study period.

Indicadores geoespaciais para avaliação do impacto ambiental da suinocultura no licenciamento em âmbito municipal

The objective of this work was to propose and evaluate geospatial indicators to analyze the environmental impact of swine production on licensing at municipal level. The study was conducted in the municipality of Quinze de Novembro, in the state of Rio Grande do Sul, Brazil. Environmental indicators in farmlands and watersheds were evaluated. A geospatial database was created using Geographic Information Systems and a systematic survey of swine farmers and farmlands receiving pig slurry, which included swine farms, current land use, soil types, drainage net, and digital elevation model. The geospatial indicators obtained with the geoprocessing tools were: watershed areas with application of swine slurry; areas of permanent preservation and for recovery; application rate of swine slurry; slope; resistance to degradation; distance between farmlands with swine slurry application and watercourse; width of potential buffer zone between farmlands and watercourse; distance between swine barns and watercourse; conflict between facilities and preservation areas; and areas that need environmental recovery. The interpretation of these indicators enables evaluating the potential environmental impacts of swine production in farmlands and watersheds where they are located, which allows to identify the locations with greater risks and to aid the environmental licensing of this activity.