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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IOP Conference Series: Earth and Environmental Science
Hermansah, H., Sandi, N., & Naspendra, Z. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/issn.1755-1315 · Cited by 12,775
The Tacit Dimension
Polanyi, M. (1997) · Elsevier eBooks · doi:10.1016/b978-0-7506-9718-7.50010-x · Cited by 12,410
Food Security: The Challenge of Feeding 9 Billion People
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010) · Science · doi:10.1126/science.1185383 · Cited by 11,943
Precision Agriculture and Food Security
Gebbers, R., & Adamchuk, V. I. (2010) · Science · doi:10.1126/science.1183899 · Cited by 1,750
A global spectral library to characterize the world's soil
Rossel, R. A. V., Behrens, T., Ben‐Dor, E., Brown, D. J., Demattê, J. A. M., Shepherd, K. D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V. I., et al. (2016) · Earth-Science Reviews · doi:10.1016/j.earscirev.2016.01.012 · Cited by 882
On-the-go soil sensors for precision agriculture
Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2004.03.002 · Cited by 848
Radically Rethinking Agriculture for the 21st Century
Fedoroff, N. V., Battisti, D. S., Beachy, R. N., Cooper, P. J. M., Fischhoff, D. A., Hodges, C. N., Knauf, V. C., Lobell, D. B., Mazur, B. J., Molden, D. J., et al. (2010) · Science · doi:10.1126/science.1186834 · Cited by 831
Digital Soil Map of the World
Sanchez, P. A., Ahamed, S., Carré, F., Hartemink, A. E., Hempel, J. W., Huising, J., Lagacherie, P., McBratney, A. B., McKenzie, N. J., Mendonça-Santos, M. D. L., et al. (2009) · Science · doi:10.1126/science.1175084 · Cited by 652
Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring
Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M. J., Bachmann, M., Barthès, B., Dor, E. B., Brown, D. J., Clairotte, M., Csorba, Á., et al. (2015) · Advances in agronomy · doi:10.1016/bs.agron.2015.02.002 · Cited by 520
Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time
Rossel, R. A. V., Adamchuk, V. I., Sudduth, K. A., McKenzie, N. J., & Lobsey, C. R. (2011) · Advances in agronomy · doi:10.1016/b978-0-12-386473-4.00005-1 · Cited by 368

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Coates, 2014, A review of new small-scale technologies for near infrared measurements, Am. Pharm. Rev.
El Bassam, 1990, vol. 42, 11

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“Many are available commercially, and some are under development (Viscarra Rossel and Bouma 2016).”
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“…(UV/Vis/ NIR/MIR), and gamma-ray spectroscopy, are effective and convenient for estimating soil properties in complex field conditions, but they are mostly used in scientific researches for their expensive cost and complex operation (Adamchuk et al., 2015; Viscarra Rossel and Bouma, 2016).”
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“…conditions, offering greater temporal and spatial ecological relevance than infrequent, few measurements from conventional laboratory procedures (Viscarra Rossel and Bouma, 2016; van Leeuwen et al., 2022; Haney et al., 2018) Spectroscopy, for example, captures molecular-level information about…”
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Background
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Self-citation
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Background
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“…(hawken, 2017). they have also been employed for soil sensing, weed identification, and disease diagnosis (Chang & islam, 2000; liakos et al., 2018; rossel & Bouma, 2016; Wahabzada et al., 2016). additionally, Ml and ann contribute to macroeconomic modeling and prediction of agricultural…”
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“…Systems (DSS) with precision agriculture (PA), which have historically consisted of a suite of data-intensive tools that help farmers make timely and targeted decisions across crop, livestock, and environmental management (Finger et al., 2019; Lioutas et al., 2019; Viscarra Rossel and Bouma, 2016).”
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Sharma, R., & Sharma, V. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27465-7.00013-3 · Cited by 1
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Agarwal, H., Kaur, S. D., Kataria, S., Roy, S., Chaudhary, D., Shukla, S., Pandey, R., Tyagi, J., & Joshi, N. C. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.006 · Cited by 1
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“Furthermore, sensor networks are also capable of characterizing parameters related to water quality (Adu-Manu et al., 2017), soil parameters (Viscarra Rossel and Bouma, 2016) and other environmental parameters like noise (D’Hondt et al., 2013).”
Background
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Which and How Many Soil Sensors are Ideal to Predict Key Soil Properties: A Case Study with Seven SensorsPreprint
Schmidinger, J., Barkov, V., Tavakoli, H., Correa, J. E., Ostermann, M., Atzmueller, M., Gebbers, R., & Vogel, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4844780 · Cited by 1
Segmentação geomorfométrica associada com tipos de solos via geotecnologias
Marques, K. P. P. (2017) · doi:10.11606/d.11.2017.tde-26072017-110043
Soil property spatial & temporal variability sensing for precision agriculture
Chisholm, G., Leveneur, J., Kennedy, J. V., & Futter, J. (2017) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip.2017.8211494
“The world’s rapidly growing population is expected to reach 9 billion people by 2050 [1].”
BackgroundInfluential
Comparison of the Performance of Two Vis-NIR Spectrometers in the Prediction of Various Soil Properties
Marmette, M.-C., Adamchuk, V. I., Nault, J., Tabatabai, S., & Cocciardi, R. A. (2018)
Transitioning from precision to decision horticulture: technology landscape
Khot, L. R. (2020) · Acta Horticulturae · doi:10.17660/actahortic.2020.1279.29
Uso de técnicas espectroscópicas no infravermelho, fluorescência induzida por laser e LIBS para a determinação de atributos químicos e físicos do solo
Freitas, V. D. S. (2020) · doi:10.11606/t.75.2020.tde-11012021-173534
Soil Parameter Mapping in Sloped Citrus Field Using a Portable Spectrophotometer
Li, Q., Usui, K., Kodaira, M., Umeda, H., & Shibusawa, S. (2018) · doi:10.11357/jsamfe.80.1_43
Development of a numeric on-line decision support system for crop fertilizer optimization
Shinde, S. (2018) · eScholarship@McGill (McGill) · doi:10.82308/32267

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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