Characterization of soils cultivated with cassava under different managements

The crop-livestock integration (CLI) and crop-livestock-forest integration (CLFI) management systems, have been shown to be viable approaches for increasing carbon sequestration in soils, resulting in the improvement of physical and chemical soil attributes. The objective of this study was to evaluate the chemical attributes and organic matter in soils under Natural Forest (NF) converted to different uses and managed differently: rotational pasture area (PAST), crop-livestock integration (CLI), and crop-livestock-forest integration (CLIF). The research was conducted at the São Paulo farm, in Iracema, located in the south-central region of the state of Roraima, Brazil. The studied soil type was classified as Ultisol. Soil samples were taken by opening ditches and examining layers at 0.1-m depth intervals from surface to 0.60-m depth. Total organic carbon (TOC), chemical and granulometric fractionation of soil organic matter (SOM), oxidizable fractions, and light organic matter in water were analyzed. Our results showed low levels of the analyzed chemical elements, a characteristic of a soil with low natural fertility. This matches conditions inherent in source material, weathered by high rainfall, a warm and humid climate, and flat topographic relief. In the 0-0.1 m layer, the PAST and CLI systems had the highest TOC contents relative to the other systems studied. At other depths, there were no statistical differences among TOC levels. The highest concentration of C in the particulate fraction (POC) was noted in the surface layer in all management systems. The pasture system had the highest concentration POC in the top 0.10 m. Our results also showed that the upper 0.10 m of soil in NF contained the lowest content of organic carbon associated with mineral (MOC) relative to the managed agrosystems. In addition, humin provided the largest contribution to SOM in all evaluated management systems. The crop-livestock integration (CLI) and crop-livestock integration forest (CLIF) systems, emerged as a strong alternative to carbon incorporation and subsequently the improvement of physical and chemical soil attributes. The objective of this work to evaluate the chemical attributes and organic matter in soils under Natural forest (NF) converted into different use and management systems: pasture (PAST), crop-livestock Integration (CLI) and crop-livestock Integration forest (CLIF). The research was conducted at São Paulo farm in Iracema, located in the Center-South region of the State of Roraima, Brazil. The soil studied was classified as Argissolo Amarelo Distrófico. The samples were taken by the opening of trenches in layers of 0-0.10, 0.10- 0.20, 0.20- 0.40, and 0.40-0.60 m depth. Total organic carbon (TOC), chemical and granulometric fractionation of soil organic matter (SOM), oxidizable fractions and organic matter in water were analyzed. The results showed low levels of the analyzed chemical elements which characterizes soils with low natural fertility, which matches the conditions of the source material, high rainfall and regional temperature, as well as the flat local relief. In the 0-0.1 m layer, the PAST and CLI systems had the highest TOC contents when compared to the other systems studied, in the other depths there were no statistical differences between the TOC levels. The highest amount of C in the particulate fraction (COp) was verified in the surface layer in all evaluated management systems. The pasture area was the system with the greatest contribution of COp to the depth of 0-0.0 m. In relation to the carbon content associated with minerals (COam), the results showed that the depth of 0-0.05 m NF area presented the lowest levels when compared to the other systems. Regarding the humic substances, there was a larger contribution of humin in all evaluated systems.

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Molecular Characterization of Cassava Mosaic Viruses and Current Mosaic Disease Concern in Three Major Cassava Production Areas in Côte D’ivoire

International Journal of Plant Pathology ◽

10.3923/ijpp.2021.12.20 ◽

2021 ◽

Vol 12 (1) ◽

pp. 12-20

Author(s):

F. Yao ◽

M. Koffi ◽

I. Abe ◽

M. N`Djetchi ◽

T. Konan ◽

...

Keyword(s):

Molecular Characterization ◽

Cote D'ivoire ◽

Côte D’Ivoire ◽

Mosaic Disease ◽

Cote D’Ivoire ◽

Côte D'ivoire ◽

Cassava Production ◽

Production Areas

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Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

Remote Sensing ◽

10.3390/rs11242905 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2905 ◽

Cited By ~ 8

Author(s):

Raúl R. Poppiel ◽

Marilusa P. C. Lacerda ◽

José L. Safanelli ◽

Rodnei Rizzo ◽

Manuel P. Oliveira ◽

...

Keyword(s):

Model Performance ◽

Google Earth ◽

Natural Fertility ◽

Soil Maps ◽

Large Area ◽

Soil Attributes ◽

Soil Variables ◽

Midwest Region ◽

Chemical Attributes ◽

Physical And Chemical

The Midwest region in Brazil has the largest and most recent agricultural frontier in the country where there is no currently detailed soil information to support the agricultural intensification. Producing large-extent digital soil maps demands a huge volume of data and high computing capacity. This paper proposed mapping surface and subsurface key soil attributes with 30 m-resolution in a large area of Midwest Brazil. These soil maps at multiple depth increments will provide adequate information to guide land use throughout the region. The study area comprises about 851,000 km2 in the Cerrado biome (savannah) in the Brazilian Midwest. We used soil data from 7908 sites of the Brazilian Soil Spectral Library and 231 of the Free Brazilian Repository for Open Soil Data. We selected nine key soil attributes for mapping and aggregated them into three depth intervals: 0–20, 20–60 and 60–100 cm. A total of 33 soil predictors were prepared using Google Earth Engine (GEE), such as climate and geologic features with 1 km-resolution, terrain and two new covariates with 30 m-resolution, based on satellite measurements of the topsoil reflectance and the seasonal variability in vegetation spectra. The scorpan model was adopted for mapping of soil variables using random forest regression (RF). We used the model-based optimization by tuning RF hyperparameters and calculated the scaled permutation importance of covariates in R software. Our results were promising, with a satisfactory model performance for physical and chemical attributes at all depth intervals. Elevation, climate and topsoil reflectance were the most important covariates in predicting sand, clay and silt. In general, for predicting soil chemical attributes, climatic variables, elevation and vegetation reflectance provided to be the most important of predictive components, while for organic matter it was a combination of climatic dynamics and reflectance bands from vegetation and topsoil. The multiple depth maps showed that soil attributes largely varied across the study area, from clayey to sandy, suggesting that less than 44% of the studied soils had good natural fertility. We concluded that key soil attributes from multiple depth increments can be mapped using Earth observations data and machine learning methods with good performance.

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Chemical and physical fractions of soil organic matter under various management regimes in Roraima, Brazil

Semina Ciências Agrárias ◽

10.5433/1679-0359.2017v38n4suplp2419 ◽

2017 ◽

Vol 38 (4Supl1) ◽

pp. 2419

Author(s):

Marden Daniel Espinoza Guardiola ◽

José Frutuoso Vale Júnior ◽

Edmilson Evangelista da Silva ◽

Celeste Queiroz Rossi ◽

Marcos Gervasio Pereira

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Organic Carbon ◽

Chemical Elements ◽

The Other ◽

Management Systems ◽

Natural Fertility ◽

Chemical Attributes ◽

Pasture Area ◽

Physical And Chemical

The crop-livestock integration (CLI) and crop-livestock-forest integration (CLFI) management systems, have been shown to be viable approaches for increasing carbon sequestration in soils, resulting in the improvement of physical and chemical soil attributes. The objective of this study was to evaluate the chemical attributes and organic matter in soils under Natural Forest (NF) converted to different uses and managed differently: rotational pasture area (PAST), crop-livestock integration (CLI), and crop-livestock-forest integration (CLIF). The research was conducted at the São Paulo farm, in Iracema, located in the south-central region of the state of Roraima, Brazil. The studied soil type was classified as Ultisol. Soil samples were taken by opening ditches and examining layers at 0.1-m depth intervals from surface to 0.60-m depth. Total organic carbon (TOC), chemical and granulometric fractionation of soil organic matter (SOM), oxidizable fractions, and light organic matter in water were analyzed. Our results showed low levels of the analyzed chemical elements, a characteristic of a soil with low natural fertility. This matches conditions inherent in source material, weathered by high rainfall, a warm and humid climate, and flat topographic relief. In the 0-0.1 m layer, the PAST and CLI systems had the highest TOC contents relative to the other systems studied. At other depths, there were no statistical differences among TOC levels. The highest concentration of C in the particulate fraction (POC) was noted in the surface layer in all management systems. The pasture system had the highest concentration POC in the top 0.10 m. Our results also showed that the upper 0.10 m of soil in NF contained the lowest content of organic carbon associated with mineral (MOC) relative to the managed agrosystems. In addition, humin provided the largest contribution to SOM in all evaluated management systems. The crop-livestock integration (CLI) and crop-livestock integration forest (CLIF) systems, emerged as a strong alternative to carbon incorporation and subsequently the improvement of physical and chemical soil attributes. The objective of this work to evaluate the chemical attributes and organic matter in soils under Natural forest (NF) converted into different use and management systems: pasture (PAST), crop-livestock Integration (CLI) and crop-livestock Integration forest (CLIF). The research was conducted at São Paulo farm in Iracema, located in the Center-South region of the State of Roraima, Brazil. The soil studied was classified as Argissolo Amarelo Distrófico. The samples were taken by the opening of trenches in layers of 0-0.10, 0.10- 0.20, 0.20- 0.40, and 0.40-0.60 m depth. Total organic carbon (TOC), chemical and granulometric fractionation of soil organic matter (SOM), oxidizable fractions and organic matter in water were analyzed. The results showed low levels of the analyzed chemical elements which characterizes soils with low natural fertility, which matches the conditions of the source material, high rainfall and regional temperature, as well as the flat local relief. In the 0-0.1 m layer, the PAST and CLI systems had the highest TOC contents when compared to the other systems studied, in the other depths there were no statistical differences between the TOC levels. The highest amount of C in the particulate fraction (COp) was verified in the surface layer in all evaluated management systems. The pasture area was the system with the greatest contribution of COp to the depth of 0-0.0 m. In relation to the carbon content associated with minerals (COam), the results showed that the depth of 0-0.05 m NF area presented the lowest levels when compared to the other systems. Regarding the humic substances, there was a larger contribution of humin in all evaluated systems.

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Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

Applied Spectroscopy ◽

10.1177/0003702818788878 ◽

2018 ◽

Vol 72 (12) ◽

pp. 1774-1780 ◽

Cited By ~ 7

Author(s):

Irene Marivel Nolasco Perez ◽

Amanda Teixeira Badaró ◽

Sylvio Barbon ◽

Ana Paula AC Barbon ◽

Marise Aparecida Rodrigues Pollonio ◽

...

Keyword(s):

Machine Learning ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Support Vector ◽

Processing Industry ◽

Chemical Attributes ◽

Physical And Chemical

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical–chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900–1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

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Characterization of defects in tabular silver halide grains

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100178367 ◽

1990 ◽

Vol 48 (4) ◽

pp. 1046-1047

Author(s):

C. Goessens ◽

D. Schryvers ◽

J. Van Landuyt ◽

A. Verbeeck ◽

R. De Keyzer

Keyword(s):

Radiation Damage ◽

Silver Halide ◽

Chemical Characteristics ◽

X Ray ◽

Free Region ◽

Central Defect ◽

Crystallographic Defects ◽

Physical And Chemical ◽

Two Sides

Silver halide grains (AgX, X=Cl,Br,I) are commonly recognized as important entities in photographic applications. Depending on the preparation specifications one can grow cubic, octahedral, tabular a.o. morphologies, each with its own physical and chemical characteristics. In the present study crystallographic defects introduced by the mixing of 5-20% iodide in a growing AgBr tabular grain are investigated. X-ray diffractometry reveals the existence of a homogeneous Ag(Br1-xIx) region, expected to be formed around the AgBr kernel. In fig. 1 a two-beam BF image, taken at T≈100 K to diminish radiation damage, of a triangular tabular grain is presented, clearly showing defect contrast fringes along four of the six directions; the remaining two sides show similar contrast under relevant diffraction conditions. The width of the central defect free region corresponds with the pure AgBr kernel grown before the mixing with I. The thickness of a given grain lies between 0.15 and 0.3 μm: as indicated in fig. 2 triangular (resp. hexagonal) grains exhibit an uneven (resp. even) number of twin interfaces (i.e., between + and - twin variants) parallel with the (111) surfaces. The thickness of the grains and the existence of the twin variants was confirmed from CTEM images of perpendicular cuts.

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