Soil sensing: A new paradigm for agriculture

QR Code

Soil sensing: A new paradigm for agriculture

Last century, during the ‘Green Revolution’ the use of synthetic fertilizers contributed to increased agricultural production. However, their use did not reflect local soil and water conditions because recommendations were developed for larger agro-ecological zones. They only focused on increased pr...

Full description

Bibliographic Details
Main Authors:	Viscarra Rossel, Raphael, Bouma, J.
Format:	Journal Article
Published:	Elsevier 2016
Online Access:	http://hdl.handle.net/20.500.11937/74697

Similar Items

Applications of proximal sensing to soil science
by: Viscarra Rossel, Raphael
Published: (2014)

Proximal sensing for soil carbon accounting
by: England, J., et al.
Published: (2018)

Sensing of soil bulk density for more accurate carbon accounting
by: Lobsey, C., et al.
Published: (2016)

Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
by: Wetterlind, J., et al.
Published: (2022)

A new detailed map of total phosphorus stocks in Australian soil
by: Viscarra Rossel, Raphael, et al.
Published: (2016)

Selected papers from the Third Global Workshop on Proximal Soil Sensing 2013
by: Viscarra Rossel, Raphael, et al.
Published: (2015)

Proximal spectral sensing in pedological assessments: vis–NIR spectra for soil classification based on weathering and pedogenesis
by: Terra, F., et al.
Published: (2018)

Proximal sensing of Cu in soil and lettuce using portable X-ray fluorescence spectrometry
by: Sacristán, D., et al.
Published: (2016)

Accounting for the effects of water and the environment on proximally sensed vis-NIR soil spectra and their calibrations
by: Ji, W., et al.
Published: (2015)

Can the sequestered carbon in agricultural soil be maintained with changes in management, temperature and rainfall? A sensitivity assessment
by: Luo, Z., et al.
Published: (2016)

Toxicity and bioaccumulation of Cu in an accumulator crop (Lactuca sativa L.) in different Australian agricultural soils
by: Sacristán, D., et al.
Published: (2015)

Proximal soil sensing of soil texture and organic matter with a prototype portable mid-infrared spectrometer
by: Dhawale, N., et al.
Published: (2015)

Guest Editorial: Near infrared spectroscopy for a better understanding of soil
by: Viscarra Rossel, Raphael, et al.
Published: (2016)

Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition
by: Viscarra Rossel, Raphael, et al.
Published: (2017)

The cost-efficiency and reliability of two methods for soil organic C accounting
by: Viscarra Rossel, Raphael, et al.
Published: (2018)

Soil organic carbon and its fractions estimated by visible-near infrared transfer functions
by: Viscarra Rossel, Raphael, et al.
Published: (2015)

Updating a national soil classification with spectroscopic predictions and digital soil mapping
by: Teng, H., et al.
Published: (2018)

Continental soil drivers of ammonium and nitrate in Australia
by: Lee, Juhwan, et al.
Published: (2018)

Spectral fusion by Outer Product Analysis (OPA) to improve predictions of soil organic C
by: Terra, F., et al.
Published: (2019)

Improved estimates of organic carbon using proximally sensed vis-NIR spectra corrected by piecewise direct standardization
by: Ji, W., et al.
Published: (2015)

Baseline estimates of soil organic carbon by proximal sensing: Comparing design-based, model-assisted and model-based inference
by: Viscarra Rossel, Raphael, et al.
Published: (2016)

Developing the Australian mid-infrared spectroscopic database using data from the Australian Soil Resource Information System
by: Hicks, W., et al.
Published: (2015)

Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis-NIR and mid-IR reflectance data
by: Terra, F., et al.
Published: (2015)

Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields
by: Ji, W., et al.
Published: (2016)

New paradigm in agriculture: issues & challenges for the developing countries
by: Mohamed Arshad, Fatimah, et al.
Published: (2010)

Multi-scale digital soil mapping with deep learning
by: Behrens, T., et al.
Published: (2018)

Curvelet transform to study scale-dependent anisotropic soil spatial variation
by: Biswas, A., et al.
Published: (2014)

Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change
by: Viscarra Rossel, Raphael, et al.
Published: (2014)

Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions
by: Ji, W., et al.
Published: (2016)

Baseline map of soil organic carbon in Tibet and its uncertainty in the 1980s
by: Zhou, Y., et al.
Published: (2019)

Australian net (1950s-1990) soil organic carbon erosion: Implications for CO2 emission and land-atmosphere modelling
by: Chappell, A., et al.
Published: (2014)

The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project
by: Viscarra Rossel, Raphael, et al.
Published: (2015)

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations
by: Shi, Z., et al.
Published: (2014)

Soil and landscape grid of Australia
by: Grundy, M., et al.
Published: (2015)

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library
by: Shi, Z., et al.
Published: (2015)

Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls
by: Viscarra Rossel, Raphael, et al.
Published: (2019)

Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths
by: Vasques, G., et al.
Published: (2014)

Baseline map of soil organic matter in China and its associated uncertainty
by: Liang, Z., et al.
Published: (2019)

Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework
by: Lee, Juhwan, et al.
Published: (2021)

Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon
by: Roudier, P., et al.
Published: (2017)

Cannot write session to /tmp/vufind_sessions/sess_qvkdiads6gr136b4hq66hd7017