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

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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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“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 30
“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 42
Geospatial modeling and mapping of soil organic carbon and texture from spectroradiometric data in Nile basin
Tiruneh, G. A., Meshesha, D. T., Adgo, E., Tsunekawa, A., Haregeweyn, N., Fenta, A. A., Alemayehu, T. Y., Ayana, G., Reichert, J. M., & Tilahun, K. (2022) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2022.100879 · Cited by 26
Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia
Islam, M. M., Alharthi, M., Alkadi, R. S., Islam, M. R., & Masum, A. K. M. (2024) · AIMS Agriculture and Food · doi:10.3934/agrfood.2024053 · Cited by 33
Systematic Stakeholder Inclusion in Digital Agriculture: A Framework and Application to Canada
Ebrahimi, H. P., Schillo, R. S., & Bronson, K. (2021) · Sustainability · doi:10.3390/su13126879 · Cited by 25
“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].”
Background
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
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
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
Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation
Shen, Z., Rossel, R. A. V., D’Agui, H. M., Walden, L., Zhang, M., Yiu, T. M., Dixon, K. W., Nevill, P., Cross, A. T., Matangulu, M., et al. (2022) · SOIL · doi:10.5194/soil-8-467-2022 · Cited by 24
“…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).”
Background
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
“…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
Wireless Sensor Networks Applied to Precision Agriculture: A worldwide literature review with emphasis on Latin America
Huerta, M., García-Cedeño, A., Guillermo, J., & Clotet, R. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3044235 · Cited by 23
A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture#
Leenen, M., Pätzold, S., Tóth, G., & Welp, G. (2022) · Journal of Plant Nutrition and Soil Science · doi:10.1002/jpln.202100031 · Cited by 23
Effects of irrigation scheduling approaches on soil moisture and vegetable production in the Northeastern U.S.A.
Schattman, R. E., Jean, H., Faulkner, J. W., Maden, R., McKeag, L., Nelson, K. C., Grubinger, V. P., Burnett, S. E., Erich, M. S., & Ohno, T. (2023) · Agricultural Water Management · doi:10.1016/j.agwat.2023.108428 · Cited by 23
Accelerating adoption of species-agnostic plant sensors for precision farming
Bukhamsin, A., Kosel, J., Blilou, I., & Salama, K. N. (2025) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00131-9 · Cited by 24
“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
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
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
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
“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
Landscape-scale spatial variability of kaolinite-gibbsite ratio in tropical soils detected by diffuse reflectance spectroscopy
Fernandes, K. L., Júnior, J. M., Bahia, A. S. R. D. S., Demattê, J. A. M., & Ribon, A. A. (2020) · CATENA · doi:10.1016/j.catena.2020.104795 · Cited by 20
In Situ Plant Sensors: Toward Real-Time, High-Resolution Monitoring
He, T., Wang, J., Chae, E., & Lee, C. (2025) · ACS Sensors · doi:10.1021/acssensors.5c01494 · Cited by 19
Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning: From prediction to explainability
Albinet, F., Peng, Y., Eguchi, T., Smolders, E., & Dercon, G. (2022) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2022.10.001 · Cited by 19
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
Self-citation
Quantification of organic carbon concentrations and stocks of tidal marsh sediments via mid-infrared spectroscopy
de Broek, M. V., & Govers, G. (2018) · Geoderma · doi:10.1016/j.geoderma.2018.09.051 · Cited by 19
Evolution of Agritech Business 4.0 – Architecture and Future Research Directions
Sivakumar, E., & Chawla, P. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/775/1/012011 · Cited by 19
“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
Kearnes, M., & RICKARDS, L. A. (2020) · Bloomsbury Publishing Plc eBooks · doi:10.5040/9781350109568.ch-005 · Cited by 19
Quantitative mapping and spectroscopic characterization of particulate organic matter fractions in soil profiles with imaging VisNIR spectroscopy
Steffens, M., Zeh, L., Rogge, D. M., & Buddenbaum, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-95298-8 · Cited by 17
Optimized self-adaptive model for assessment of soil organic matter using Fourier transform mid-infrared photoacoustic spectroscopy
Ma, F., Du, C., Zhou, J., & Shen, Y. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.09.017 · Cited by 17
Current Advances in Internet of Underground Things
Salam, A., & Raza, U. A. (2020) · Springer eBooks · doi:10.1007/978-3-030-50861-6_10 · Cited by 16

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: Life Sciences › Agricultural and Biological Sciences › Soil Science › 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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