Integração de sensores geofísicos e geoestatística para mapear atributos do solo

O conhecimento da variação espacial dos atributos do solo é primordial para o gerenciamento do sistema agrícola. O objetivo do trabalho foi mapear, em uma área de 3,4 ha em Seropédica, RJ, a condutividade elétrica aparente, susceptibilidade magnética e os teores dos elementos radioativos tório e urânio do solo, medidos por sensores geofísicos in situ, e os teores de argila, ferro, carbono orgânico e umidade, e capacidade de troca catiônica do solo, medidos em laboratório, em 130 pontos amostrais. Comparou-se krigagem ordinária com krigagem universal utilizando as coordenadas geográficas x e y e a elevação como covariáveis. Os dois métodos de krigagem produziram mapas com padrão de distribuição espacial e índices de incerteza semelhantes. Outrossim, os padrões de dependência e distribuição espacial foram similares entre os atributos geofísicos e os de laboratório, evidenciando o potencial da geofísica para o mapeamento de atributos do solo.

Quantification of soil organic matter using mathematical models based on colorimetry in the Munsell color system

O presente estudo teve como objetivo desenvolver modelos matemáticos para a quantificação do teor de matéria orgânica, a partir da cor do solo, obtida por aparelho colorímetro no sistema Munsell de cores. Para esse fim, 912 amostras de solo foram coletadas na região de Porto Grande (Amapá) e enviadas para análises química, granulométrica e determinação da cor em amostras secas e úmidas. Os componentes valor e croma da cor do solo no sistema Munsell, obtidos por colorímetro, foram utilizados para quantificar através de regressão múltipla passo a passo (stepwise) o teor de matéria orgânica do solo. O modelo de predição com base em todas as amostras apresentou R² de 0,66 para amostras úmidas e 0,56 para amostras secas, ao serem validados utilizando amostras independentes. Foi possível ainda melhorar os modelos quando as amostras foram separadas por classe de solo ou textura, e os modelos gerados com base em cores de amostras úmidas foram sistematicamente superiores àqueles utilizando amostras secas. Em relação às classes de solo, os melhores resultados foram obtidos para Argissolos e Latossolos, ambos gerando um R² de validação independente de 0,73 (amostra úmida). Para textura, os melhores resultados foram obtidos para solos de textura muito argilosa, com R² de validação de 0,81 (amostra úmida). Os modelos de predição de matéria orgânica em função da cor do solo possuem simplicidade e potencial para serem utilizados no laboratório e no campo, especialmente para Argissolos e Latossolos de textura argilosa, de maneira automática e sem necessidade de uso de produtos ou reagentes.This study aimed to derive mathematical models to predict the soil organic matter content based on soil color obtained by a colorimeter in the Munsell color system. A total of 907 soil samples were collected in the region of Porto Grande (Amapá, Brazil) and analyzed in the laboratory for chemical properties, particle size distribution and color of dry and wet samples. The Munsell color components value and croma obtained using a colorimeter were used to predict soil organic matter content based on stepwise multiple linear regression. Models derived using all samples had R² of 0.66 for wet samples and 0.56 for dry samples, respectively, when validated using independent samples. It was possible to improve the models by separating the samples by soil class or texture. The models derived using colors obtained from wet samples were systematically better than those based on dry samples. Among soil classes, best results were obtained for Argissolos (Ultisols) and Latossolos (Oxisols), both having an R² of independent validation of 0.73 (wet sample). For texture, best results were obtained for very clayey soils, with an R² of validation of 0.81 (wet sample). The soil organic matter prediction models based on soil color have simplicity and potential to be used in the laboratory and in the field with quick and unnecessary chemical products, especially for Ultisols and Oxisols of clayey texture.CNPq/PQ e CNPq/PIBI

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

Time course and mechanisms of left ventricular systolic and diastolic dysfunction in monocrotaline-induced pulmonary hypertension

Although pulmonary hypertension (PH) selectively overloads the right ventricle (RV), neuroendocrine activation and intrinsic myocardial dysfunction have been described in the left ventricle (LV). In order to establish the timing of LV dysfunction development in PH and to clarify underlying molecular changes, Wistar rats were studied 4 and 6 weeks after subcutaneous injection of monocrotaline (MCT) 60 mg/kg (MCT-4, n = 11; MCT-6, n = 11) or vehicle (Ctrl-4, n = 11; Ctrl-6, n = 11). Acute single beat stepwise increases of systolic pressure were performed from baseline to isovolumetric (LVPiso). This hemodynamic stress was used to detect early changes in LV performance. Neurohumoral activation was evaluated by measuring angiotensin-converting enzyme (ACE) and endothelin-1 (ET-1) LV mRNA levels. Cardiomyocyte apoptosis was evaluated by TUNEL assay. Extracellular matrix composition was evaluated by tenascin-C mRNA levels and interstitial collagen content. Myosin heavy chain (MHC) composition of the LV was studied by protein quantification. MCT treatment increased RV pressures and RV/LV weight ratio, without changing LV end-diastolic pressures or dimensions. Baseline LV dysfunction were present only in MCT-6 rats. Afterload elevations prolonged tau and upward-shifted end-diastolic pressure dimension relations in MCT-4 and even more in MCT-6. MHC-isoform switch, ACE upregulation and cardiomyocyte apoptosis were present in both MCT groups. Rats with severe PH develop LV dysfunction associated with ET-1 and tenascin-C overexpression. Diastolic dysfunction, however, could be elicited at earlier stages in response to hemodynamic stress, when only LV molecular changes, such as MHC isoform switch, ACE upregulation, and myocardial apoptosis were present.Supported by Portuguese grants from FCT (POCI/SAU-FCF/60803/2004 and POCI/SAU-MMO/61547/2004) through Cardiovascular R&D Unit (FCT No. 51/94)

Soil Phosphorus Landscape Models for Precision Soil Conservation

Phosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a precision soil conservation approach combining finescale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes

Quantificação de matéria orgânica do solo através de modelos matemáticos utilizando colorimetria no sistema Munsell de cores

O presente estudo teve como objetivo desenvolver modelos matemáticos para a quantificação do teor de matéria orgânica, a partir da cor do solo, obtida por aparelho colorímetro no sistema Munsell de cores. Para esse fim, 912 amostras de solo foram coletadas na região de Porto Grande (Amapá) e enviadas para análises química, granulométrica e determinação da cor em amostras secas e úmidas. Os componentes valor e croma da cor do solo no sistema Munsell, obtidos por colorímetro, foram utilizados para quantificar através de regressão múltipla passo a passo (stepwise) o teor de matéria orgânica do solo. O modelo de predição com base em todas as amostras apresentou R² de 0,66 para amostras úmidas e 0,56 para amostras secas, ao serem validados utilizando amostras independentes. Foi possível ainda melhorar os modelos quando as amostras foram separadas por classe de solo ou textura, e os modelos gerados com base em cores de amostras úmidas foram sistematicamente superiores àqueles utilizando amostras secas. Em relação às classes de solo, os melhores resultados foram obtidos para Argissolos e Latossolos, ambos gerando um R² de validação independente de 0,73 (amostra úmida). Para textura, os melhores resultados foram obtidos para solos de textura muito argilosa, com R² de validação de 0,81 (amostra úmida). Os modelos de predição de matéria orgânica em função da cor do solo possuem simplicidade e potencial para serem utilizados no laboratório e no campo, especialmente para Argissolos e Latossolos de textura argilosa, de maneira automática e sem necessidade de uso de produtos ou reagentes

The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges

Made available in DSpace on 2019-10-06T16:42:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-15Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The present study was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350–2500 nm range of Brazilian soils. The Brazilian Soil Spectral Library (BSSL) began in 1995, creating a protocol to gather soil samples from different locations in Brazil. The BSSL reached 39,284 soil samples from 65 contributors representing 41 institutions from all 26 states. Through the BSSL spectra database, it was possible to estimate important soil attributes, such as clay, sand, soil organic carbon, cation exchange capacity, pH and base saturation, resulting in differences among the multi-scale models taking Brazil (overall), regional and state scale. In general, spectral descriptive and quantitative behavior indicated important relationship with physical, chemical and mineralogical properties. Statistical analyses showed that six basic patterns of spectral signatures represent the Brazilian soils types and that environmental conditions explain the differences in spectra. This study demonstrates that spectroscopy analyses along with the establishment of soil spectral libraries are a powerful technique for providing information on a national and regional levels. We also developed an interactive online platform showing soil sample locations and their contributors. As soil spectroscopy is considered a fast, simple, accurate and nondestructive analytical procedure, its application may be integrated with wet analysis as an alternative to support the sustainable management of soils.Department of Soil Science Luiz de Queiroz College of Agriculture (ESALQ) University of São Paulo (USP), Ave. Pádua Dias 11, Cx. Postal 9Department of Soil Federal University of Santa Maria, Av. Roraima 1000Geographical Sciences Department Federal University of Pernambuco, Av. Ac. Hélio Ramos, s/nDepartment of Agronomy State University of Maringá, Av. Colombo 5790Department of Agriculture Biodiversity and Forestry Federal University of Santa Catarina, Rodovia Ulysses Gaboardi 3000 - Km 3Federal Rural University of Amazon, Ave. Presidente Tancredo Neves 2501Faculty of Agronomy and Veterinary Medicine University of BrasíliaEMBRAPA - Solos, R. Antônio Falcão, 402, Boa ViagemCenter of Nuclear Energy in Agriculture (CENA) USP, Av. Centenário 303CDRS/Secretary of Agriculture of São Paulo State, R. Campos Salles 507Department of Soils Federal University of Viçosa, Ave. Peter Henry Rolfs s/nEMBRAPA – Informática Agropecuária, Ave. André Tosello, 209Department of Nuclear Energy Federal University of Pernambuco, Av. Prof. Luis Freire 1000Department of Geography Federal University of Rio Grande do Norte, R. Joaquim Gregório s/nAgronomic Institute of Campinas (IAC), Ave. Barão de Itapura 1481Institute of Agricultural Sciences Federal Rural University of Amazônia, Ave. Presidente Tancredo Neves 2501, 66.077-830Department of Soil Science Federal University of LavrasFederal University of Mato Grosso, Cuiabá, Av. Fernando Corrêa da Costa 2367Department of Soils Federal Rural University of Rio de Janeiro, Rodovia BR 465, Km 07 s/nSoil and Water Sciences Department University of Florida, 2181 McCarty Hallr, PO Box 110290EMBRAPA - Solos, R. Jardim Botânico, 1024Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/nFederal University of Sergipe, Av. Marechal Rondon s/nGraduate Program in Earth Sciences (Geochemistry) Department of Geochemistry Federal Fluminense University, Outeiro São João Batista, s/nFederal Institute of the Southeast of Minas Gerais, R. Monsenhor José Augusto 204Federal University of Rio Grande do Norte, R. Joaquim Gregório s/nFederal University of PiauíEMBRAPA Milho e Sorgo, Rod MG 424 Km 45Institute of Agricultural Sciences Federal University of Jequitinhonha e Mucuri Valleys, Ave. Ver. João Narciso 1380Department of Biosystems Engineering ESALQ USP, Ave. Pádua Dias 11, Cx. Postal 9Federal University of Acre, Rodovia BR 364 Km 04Federal University of Amazonas, Av. General Rodrigo O. J. Ramos 1200EMBRAPA Clima Temperado, BR-392, km 78Department of Agronomy Federal Rural University of Pernambuco, R. Manuel de Medeiros s/nEMBRAPA Cocais, Quadra 11, Av. São Luís Rei de França 4Paraense Emílio Goeldi Museum, Av. Gov. Magalhães Barata 376Exata Laboratory, Rua Silvestre Carvalho Q 11Federal University of Rondônia, BR 364, Km 9.5Nacional Institute for Amazonian Research, Ave. André Araújo 2936Department of Forestry Sciences ESALQ-USP, Ave. Pádua Dias 11, Cx. Postal 9Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/nFAPESP: 2014/22262-0FAPESP: 2016/26176-6FAPESP: 2017/03207-