Soil sensing: A new paradigm for agriculture

Viscarra Rossel, Raphael A.; Bouma, Johan

doi:10.1016/j.agsy.2016.07.001

Cited by 230Soil Geostatistics and MappingSmart Agriculture and AISoil Management and Crop Yield

Soil sensing: A new paradigm for agriculture

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Viscarra Rossel, Raphael A., Bouma, Johan·2016·Agricultural Systems

DOI 10.1016/j.agsy.2016.07.001

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Soil Security in Sustainable Development
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Self-citation
Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale
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Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies
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Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes
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VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing
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Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region
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Potential of apparent soil electrical conductivity to describe the soil pH and improve lime application in a clayey soil
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Estimation of soil total nitrogen using the synthetic color learning machine (SCLM) method and hyperspectral data
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Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry
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“this number, due to the expansion of agricultural areas [2], as well as the adoption of soil mapping techniques for precision agriculture (PA) practices, which demand information in a high spatial and temporal density [3].”
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Best management practices from agricultural economics: Mitigating air, soil and water pollution
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Myth or reality? The Digitalization of Climate-Smart Agriculture (DCSA) practices in smallholding agriculture in the Bono East Region of Ghana
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Agro 4.0: Enabling agriculture digital transformation through IoT
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Multi-source data fusion of big spatial-temporal data in soil, geo-engineering and environmental studies
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Influence of soil sample preparation on the quantification of NPK content via spectroscopy
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Examining the social and biophysical determinants of U.S. Midwestern corn farmers’ adoption of precision agriculture
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Coordinated development of high-quality agricultural transformation and technological innovation: a case study of main grain-producing areas, China
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Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture
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Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market
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“Consequently, classification maps obtained from traditional soil surveys can no longer meet the growing need for high-resolution mapping; thus a labour-saving and cost-effective method is needed to fill the gap (Arrouays et al., 2014; Viscarra Rossel & Bouma, 2016; Chen et al., 2018).”
Background
Review: Closing nutrient cycles for animal production – Current and future agroecological and socio-economic issues
Kronberg, S. L., Provenza, F. D., van Vliet, S., & Young, S. N. (2021) · animal · doi:10.1016/j.animal.2021.100285 · Cited by 27 · see citing works
“testing at high spatiotemporal scales (Rossel and Bouma, 2016).”
Leveraging artificial intelligence in agribusiness: a structured review of strategic management practices and future prospects
Bhat, I. A., Ansarullah, S. I., Ahmad, F., Amir, S., Sidana, S., Sinha, A., Khalid, S., & Yazdani, G. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01260-3 · Cited by 26 · see citing works
Geospatial modeling and mapping of soil organic carbon and texture from spectroradiometric data in Nile basin
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Systematic Stakeholder Inclusion in Digital Agriculture: A Framework and Application to Canada
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“These stakeholders are involved in knowledge and technology development [38], including in developing solutions for testing and monitoring specialised aspects of farm monitoring such as on fertilisers and nutrients [36].”
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Precision Viticulture: Managing vineyard variability for improved quality outcomes
Bramley, R. G. V. (2021) · Elsevier eBooks · doi:10.1016/b978-0-08-102067-8.00002-6 · Cited by 25 · see citing works
Exploring Operational Procedures to Assess Ecosystem Services at Farm Level, including the Role of Soil Health
Bouma, J., de Haan, J. J., & Dekkers, M.-F. (2022) · Soil Systems · doi:10.3390/soilsystems6020034 · Cited by 25 · see citing works
Self-citation
Precision production environments for sugarcane fields
Sanches, G. M., de Paula, M. T. N., Magalhães, P. S. G., Duft, D. G., Vitti, A. C., Kölln, O. T., Borges, B. M. M. N., & Franco, H. C. J. (2018) · Scientia Agricola · doi:10.1590/1678-992x-2017-0128 · Cited by 25 · see citing works
Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation
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“…conventional assessment is time-consuming and expensive because laboratory analyses require elaborate methods and specialised equipment and procedures and can be prone to errors resulting from inappropriate sampling, transportation, preparation, or analysis (Viscarra Rossel and Bouma, 2016).”
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Underground Ink: Printed Electronics Enabling Electrochemical Sensing in Soil
Chen, K., Kachhadiya, J., Muhtasim, S., Cai, S., Huang, J., & Andrews, J. B. (2024) · Micromachines · doi:10.3390/mi15050625 · Cited by 23 · see citing works
“…individual land farmers to indus-trialized farm managers, the challenge lies in meeting market demands while preserving the natural environment [120]. platforms facilitate connectivity and data exchange between distributed sensor nodes, enabling scalable and interoperable soil monitoring…”
Background
Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm
Cao, Y., Bao, N., Liu, S., Zhao, W., & Li, S. (2020) · Canadian Journal of Soil Science · doi:10.1139/cjss-2020-0009 · Cited by 23 · see citing works
Wireless Sensor Networks Applied to Precision Agriculture: A worldwide literature review with emphasis on Latin America
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A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture#
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Accelerating adoption of species-agnostic plant sensors for precision farming
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“Electrochemical analysis can be carried out in the field thanks to reductions in the size, cost and complexity of potentiostats 84 .”
Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors
Schmidinger, J., Barkov, V., Tavakoli, H., Correa, J., Ostermann, M., Atzmueller, M., Gebbers, R., & Vogel, S. (2024) · Geoderma · doi:10.1016/j.geoderma.2024.117017 · Cited by 21 · see citing works
One shot evaluation of NPK in soils by “electronic tongue”
Khaydukova, M. M., Kirsanov, D. O., Sarkar, S. K., Mukherjee, S., Ashina, J., Bhattacharyya, N., Chanda, S., Bandyopadhyay, R., & Legin, A. V. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106208 · Cited by 21 · see citing works
Developing Management Practices in: “Living Labs” That Result in Healthy Soils for the Future, Contributing to Sustainable Development
Bouma, J., & Veerman, C. (2022) · Land · doi:10.3390/land11122178 · Cited by 21 · see citing works
Self-citation
A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy
Breure, T., Haefele, S. M., Hannam, J., Corstanje, R., Webster, R., Moreno-Rojas, S., & Milne, A. E. (2022) · Precision Agriculture · doi:10.1007/s11119-022-09887-2 · Cited by 20 · see citing works
“The net result is substantially cheaper than chemical analysis alone and enables surveyors to obtain sufficient detail on the spatial variation of soil properties affordably (Viscarra Rossel & Bouma, 2016).”
Background
Analysis of Total Soil Nutrient Content with X-ray Fluorescence Spectroscopy (XRF): Assessing Different Predictive Modeling Strategies and Auxiliary Variables
Tavares, T. R., de Almeida, E., Pinheiro, C. R., Guerrero, A., Fiorio, P. R., & de Carvalho, H. W. P. (2023) · AgriEngineering · doi:10.3390/agriengineering5020043 · Cited by 20 · see citing works
Landscape-scale spatial variability of kaolinite-gibbsite ratio in tropical soils detected by diffuse reflectance spectroscopy
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Hydropedology and the Societal Challenge of Realizing the 2015 United Nations Sustainable Development Goals
Bouma, J. (2016) · Vadose Zone Journal · doi:10.2136/vzj2016.09.0080 · Cited by 19 · see citing works
Self-citation
Quantification of organic carbon concentrations and stocks of tidal marsh sediments via mid-infrared spectroscopy
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“Raphael A. Viscarra Rossel and Johan Bouma [4] in their work have stated that increasing food productivity using Fertilizers causes irreversible damage to environment.”
Background
Knowing Earth, Knowing Soil: Epistemological Work and the Political Aesthetics of Regenerative Agriculture
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Quantitative mapping and spectroscopic characterization of particulate organic matter fractions in soil profiles with imaging VisNIR spectroscopy
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Advanced Multi-functional Sensors for In-situ Soil Parameters for Sustainable Agriculture
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Preface to the special section “Research frontiers on the use of sensing technologies and ICT for soil and fertilizer managements (Sensing-ICT)”
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Efficient way to Sense the Soil Health based on the Policies and Guidelines to Prescribe Nutrients for better Yield
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Authors: Viscarra Rossel, R. A., & Bouma, J.
Affiliations: Wageningen University & Research (NL)
ORCID: Viscarra Rossel, Raphael A.
Bouma, Johan
Venue: Agricultural Systems
Publisher: Elsevier BV
Published: 2016-10
Volume: 148
Pages: 71-74
ISSN: 0308-521X (ISSN-L)
DOI: 10.1016/j.agsy.2016.07.001
OpenAlex: W2495691485
Cited by: 230
References: 38
Citations by year: 2026: 20 · 2025: 31 · 2024: 21 · 2023: 17 · 2022: 40 · 2021: 28 · 2020: 26 · 2019: 15 · 2018: 19 · 2017: 12 · 2016: 1
FWCI: 9.78
Type: journal article
Language: en
Open access: green
research.wur.nl
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Agricultural Systems (publishedVersion)
Socio-Environmental Systems Modeling (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
eSpace (Curtin University) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Grain Research and Development Corporation (CSA00048)
Topics: Physical Sciences › Environmental Science › Environmental Engineering › Soil Geostatistics and Mapping
Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Management and Crop Yield
Keywords: soil sensing, precision agriculture, site-specific management, soil nutrient monitoring, soil moisture monitoring, chemical fertilizer use, carbon sequestration, greenhouse gas emissions reduction, sustainable agriculture, bottom-up decision-making

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