Carlos Manoel Pedro Vaz

Brazilian Agricultural Research Corporation

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Sı́lvio Crestana

Brazilian Agricultural Research Corporation

Ricardo Yassushi Inamasu

Brazilian Agricultural Research Corporation

J. C. Franchini

Brazilian Agricultural Research Corporation

J. de M. Naime

Brazilian Agricultural Research Corporation

Eduardo Antônio Speranza

Brazilian Agricultural Research Corporation

L. H. Bassoi

Brazilian Agricultural Research Corporation

J. W. Hopmans

University of California, Davis

Renata Alves de Toledo

University of Macau

Rafael Galbieri

Universidade Federal de Mato Grosso

Sérgio Antônio Spínola Machado

National University of the Littoral

Cooperative Institutions

Brazilian Agricultural Research Corporation

117

Universidade de São Paulo

Universidade Federal de São Carlos

Agricultural Research Corporation

Universidade Estadual Paulista (Unesp)

Methodist University of Piracicaba

Universidade Brasil

University of Nebraska–Lincoln

National Council for Scientific and Technological Development

Cotton (United States)

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New Perspectives for the Application of High-Resolution Benchtop X-Ray MicroCT for Quantifying Void, Solid and Liquid Phases in Soils

Springer eBooks (2014)

Carlos Manoel Pedro Vaz Markus Tuller Paulo Renato Orlandi Lasso Sı́lvio Crestana

10.1007/978-3-319-06013-2_12

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Citations (17)

X-ray microtomography to investigate thin layers of soil clod

Soil and Tillage Research (1998)

Álvaro Macedo Sı́lvio Crestana Carlos Manoel Pedro Vaz

Microscale chemistry

X-ray microtomography

10.1016/s0167-1987(98)00180-9

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Citations (45)

Quantifying the Impact of Soil Properties on the Performance of Electromagnetic Water Content Sensors

2009 Reno, Nevada, June 21 - June 24, 2009 (2009)

Congying Wang Ricardo Estevez Carlos Manoel Pedro Vaz Scott B. Jones

Most water content sensors today rely on soil dielectric properties and measure using electromagnetic (EM) techniques. This includes for example time domain reflectometry (TDR), ground penetrating radar (GPR) and remote sensing measurements, etc. There are many different soil water content sensors available with orders of magnitude difference in price, suggesting significant differences in quality. Our objective was to highlight key factors that impact soil water content measurements using EM sensors and to develop a standard technique for evaluating the performance of EM sensors, especially to find or design a reference relaxing-conducting (R-C) system capable of providing variable electrical conductivity and relaxation frequency, which represent the most challenging measurement conditions in soils. We used network analyzer measurements to provide well-defined dielectric permittivity standards with which to characterize EM sensors. We evaluated a 50 MHz EM sensor using liquids and suspensions developed in this study which exhibit well-defined permittivity and variable electrical conductivity properties. The effective frequency analysis and error evaluation were conducted on the Hydra Probe testing results. The methodology is aimed at providing quantitative measures of sensor performance encompassing a wide variety of soil and porous media conditions.

Reflectometry

Network analyzer (electrical)

Ground-Penetrating Radar

10.13031/2013.27217

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Design and characterization of a pneumatic micro glass beads matrix sensor for soil water potential threshold control in irrigation management

Irrigation Science (2022)

Carlos Manoel Pedro Vaz Luiz Fernando Porto Carlos Ignácio D ́Alkaine L. H. Bassoi André Torre Neto

Irrigation scheduling

Matrix (chemical analysis)

10.1007/s00271-022-00791-1

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Citations (4)

Sequential Flow Injection Analysis System On-Line Coupled to High Intensity Focused Ultrasound: Green Methodology for Trace Analysis Applications As Demonstrated for the Determination of Inorganic and Total Mercury in Waters and Urine by Cold Vapor

Analytical Chemistry (2006)

C FERNANDEZ António C. L. Conceição Carlos Manoel Pedro Vaz José Luís Capelo R. Rial‐Otero

Mercury

Flow injection analysis

TRACE (psycholinguistics)

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Utilizacao da polarografia de pulso diferencial na determinacao de isotermas de adsorcao de freundlich do herbicida 2,4-d em solos

Livro de Resumos (1996)

Marli Rodrigues da Cunha Massaroppi Sérgio Antônio Spínola Machado Luiz Henrique Mazo Luís Alberto Avaca Carlos Manoel Pedro Vaz

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Experimentação on-farm no âmbito da agricultura de precisão

J. L. F. Pires Ziany Neiva Brandão Á. V. de Resende C. R. Grego E. U. Ramos

10.4322/978-65-86819-38-0.1000072

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Contribution of water content and bulk density to field soil penetration resistance as measured by a combined cone penetrometer–TDR probe

Soil and Tillage Research (2001)

Carlos Manoel Pedro Vaz L. H. Bassoi J. W. Hopmans

Penetrometer

Penetration (warfare)

Penetration test

10.1016/s0167-1987(01)00173-8

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Citations (129)

Electroanalytical procedure and sorption studies for imazaquin in different soils

International Journal of Environmental & Analytical Chemistry (2016)

Giuliane M. Castanho Renata Alves de Toledo Hojae Shim Qihong Lu Carlos Manoel Pedro Vaz

The objective of the present work is to determine sorption coefficients of the herbicide imazaquin using the differential pulse polarography in the major soil classes occurring in the State of São Paulo, Brazil. This study contributes to the predictions of pesticide fate and transport mechanisms in the environment as well as its exposure and interactions with different soil materials. Imazaquin was analysed directly in soil samples (scan rate 2 mV s–1, mercury drop size 0.37 mm, and pulse height 50 mV) and the detection limits varied from 42 to 58 g L–1 for different soils. Imazaquin showed the KF values varying from 0.8 (for the sandy soil) to 35.5 L kg–1 (for the medium textured soil), with a significant dependence on the soil pH, organic matter content, and clay minerals. The study demonstrates the practical utilisation of the electroanalytical methodology to determine the imazaquin adsorption in soil and consequently assess the impact of this herbicide in the environment, especially for the evaluation of the potential risk of surface and groundwater contamination.

Soil test

10.1080/03067319.2016.1172217

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Citations (4)

Estimating Cotton Yield in the Brazilian Cerrado Using Linear Regression Models from MODIS Vegetation Index Time Series

AgriEngineering (2024)

Daniel A. B. de Siqueira Carlos Manoel Pedro Vaz Flávio S. Silva Ednaldo José Ferreira Eduardo Antônio Speranza

Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers’ decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, and the inherent complexity and inexplicability of these models hinder their widespread adoption. This paper presents a simpler approach, employing linear regression models fitted from vegetation indices (VIs) extracted from MODIS sensor data on the Terra and Aqua satellites. The aim is to forecast cotton yields in key areas of the Brazilian Cerrado. Using data from 281 commercial production plots, models were trained (167 plots) and tested (114 plots), relating seed cotton yield to nine commonly used VIs averaged over 15-day intervals. Among the evaluated VIs, Enhanced Vegetation Index (EVI) and Triangular Vegetation Index (TVI) exhibited the lowest root mean square errors (RMSE) and the highest determination coefficients (R2). Optimal periods for in-season yield prediction fell between 90 and 105 to 135 and 150 days after sowing (DAS), corresponding to key phenological phases such as boll development, open boll, and fiber maturation, with the lowest RMSE of about 750 kg ha−1 and R2 of 0.70. The best forecasts for early crop stages were provided by models at the peaks (maximum value of the VI time series) for EVI and TVI, which occurred around 80–90 DAS. The proposed approach makes the yield predictability more inferable along the crop time series just by providing sowing dates, contour maps, and their respective VIs.

Enhanced vegetation index

10.3390/agriengineering6020054

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Citations (1)