Optimizing soil analysis in precision agriculture: Evaluating alternative methods for SOC prediction

Reyna-Bowen, José Lizardo; Montenegro, Lenin Vera; Delgado-Moreira, María Isabel

doi:10.12911/22998993/195882

Artykuł - szczegóły

Tytuł artykułu

Optimizing soil analysis in precision agriculture: Evaluating alternative methods for SOC prediction

Autorzy

Reyna-Bowen José Lizardo , Montenegro Lenin Vera , Delgado-Moreira María Isabel

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.12911/22998993/195882

Warianty tytułu

Języki publikacji

Abstrakty

Soil analysis plays a crucial role in precision agriculture, where alternatives or complementary methods to traditional laboratory analysis are needed to reduce costs and processing times. This study evaluated models from different devices for estimating soil organic carbon (SOC) using visible near-infrared (Vis-NIR) spectral data and examined the predictive performance of these models across diverse soil types and land uses. A total of 266 soil samples were collected at various depths from two dehesa farms. Soil reflectance spectra were measured using a LabSpec 5000 spectrophotometer with a contact probe and a Muglight accessory. SOC concentration was determined using the Walkley & Black method. Model prediction accuracy was assessed through metrics including the coefficient of determination (R²), residual predictive deviation (RPD), root mean squared error (RMSE), and range error ratio (RER). Cross-validation demonstrated strong predictive accuracy for SOC, with R² and RPD values exceeding 0.95 and 4.54, respectively, and RER values surpassing 20. Although external validation metrics were more conservative, they still showed excellent RPD indices above 3.12, with no significant difference between devices. Both the Muglight and contact probe yielded low RMSE values (0.222 vs. 0.244) and high R² values (0.90 vs. 0.89). These findings indicate that both devices can reliably predict SOC, with the contact probe offering the added advantage of faster spectrum recording compared to the Muglight.

Słowa kluczowe

soil fertility precision agriculture sustainable agriculture spectral sensing modeling

Wydawca

Polskie Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology

Czasopismo

Journal of Ecological Engineering

Rocznik

2025

Tom

Vol. 26, nr 1

Strony

322--331

Opis fizyczny

Bibliogr. 45 poz., rys., tab.

Twórcy

autor

Reyna-Bowen José Lizardo

jlreyna@espam.edu.ec

Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, Campus Politécnico El Limón, Calceta, Ecuador

https://orcid.org/0000-0003-0352-4005

autor

Montenegro Lenin Vera

Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, Campus Politécnico El Limón, Calceta, Ecuador

autor

Delgado-Moreira María Isabel

Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, Campus Politécnico El Limón, Calceta, Ecuador

https://orcid.org/0000-0002-3368-7481

Bibliografia

1. Arrouays D., Feller C., Jolivet C., Saby N., Andreux F., Bernoux M. (2003). Estimation de stocks de carbone organique des sols à différentes échelles d’ espace et de temps. Etude et Gestion des Sols, 10(4), 347–355.
2. Baldock J. A., Hawke B., Sanderman J., MacDonald L. M. (2013). Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra. Soil Research, 51(7–8), 577–595. https://doi.org/10.1071/SR13077
3. Barnes R. J., Dhanoa M. S., Lister S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777. https://doi.org/10.1366/0003702894202201
4. Bernoux M., da Conceição M., Volkoff B., Cerri C. C. (2002). Brazil’s Soil Carbon Stocks. Soil Science Society of America Journal, 66(3). https://doi.org/10.2136/sssaj2002.8880
5. Cao Y., Bao N., Liu S., Zhao W., Li S. (2020). Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm. Canadian Journal of Soil Science, 1–10. dx.doi.org/10.1139/cjss-2020-0009
6. Chen S. Li S. Ma W. Ji W. Xu D. Shi Z. & Zhang G. (2019). Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification. European Journal of Soil Science, 70(1), 42–53. https://doi.org/10.1111/ejss.12715
7. Chen S., Peng J., Ji W., Zhou Y., He J., Shi Z. (2016). Study on the characterization of VNIR-MIR spectra and prediction of soil organic matter in paddy soil. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu, 36(6), 1712–1716. http://www.ncbi.nlm.nih.gov/ pubmed/30052377
8. Clark R. N., King T. V., Klejwa M., Swayze G. A., Vergo N. (1990). High spectral resolution reflectance spectroscopy of minerals. Journal of Geophysical Research, 95. https://doi.org/10.1029/jb095ib08p12653
9. Douglas R. K., Nawar S., Alamar M. C., Coulon F., Mouazen A. M. (2018). Rapid detection of alkanes and polycyclic aromatic hydrocarbons in oil-contaminated soil with visible near-infrared spectroscopy. European Journal of Soil Science, 1–11. https://doi.org/10.1111/ejss.12567
10. Gandariasbeitia M., Besga G., Albizu I., Larregla S., Mendarte S. (2017). Prediction of chemical and biological variables of soil in grazing areas with visible- and near-infrared spectroscopy. Geoderma, 305, 228–235. https://doi.org/10.1016/j.geoderma.2017.05.045
11. Guillén C. E., Dávila M. J., Gilliot J. M., Vaoudour E. (2013). Aporte de la espectroscopia a la estimación de carbono orgánico de los suelos de la planicie de Versalles, Francia. Revista Geografica Venezolana, 54(1), 85–98.
12. He Y., Huang M., Garcia A., Hernandez A., Song H. (2007). Prediction of soil macronutrients content using near-infrared spectroscopy. Computers and Electronics in Agriculture, 58(2), 144–153. https://doi.org/10.1016/j.compag.2007.03.011
13. Hong Y., Yu L., Chen Y., Liu Y., Liu Y., Liu Y., Cheng H. (2017). Prediction of soil organic matter by VIS–NIR spectroscopy using normalized soil moisture index as a proxy of soil moisture. Remote Sensing, 10(1), 28. https://doi.org/10.3390/ rs10010028
14. Hosseini M., Rajabi S., Khaledian Y., Jafarzadeh H., Brevik E., Movahedi S. (2017). Comparison of multiple statistical techniques to predict soil phosphorus. Applied Soil Ecology, 114, 123–131. https://doi.org/10.1016/j.apsoil.2017.02.011
15. Huang C., Han L., Liu X., Ma L. (2011). The rapid estimation of cellulose, hemicellulose, and lignin contents in rice straw by near infrared spectroscopy. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 33(2), 114–120. https://doi.org/10.1080/15567030902937127
16. Ji W., Li S., Chen S., Shi Z., Viscarra Rossel R. A., Mouazen A. M. (2016). Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions. Soil and Tillage Research, 155, 492–500. https:// doi.org/10.1016/j.still.2015.06.004
17. Kusumo B. H. (2018). In Situ Measurement of Soil Carbon with Depth using Near Infrared (NIR) Spectroscopy. IOP Conference Series: Materials Science and Engineering, 434. https://doi.org/10.1088/1757-899X/434/1/012235
18. Kusumo B. H., Sukartono S., Bustan B. (2018a). The rapid measurement of soil carbon stock using near-infrared technology. IOP Conference Series: Earth and Environmental Science, 129(1). https://doi.org/10.1088/1755-1315/129/1/012023
19. Kusumo B. H., Sukartono S., Bustan B. (2018b). Rapid measurement of soil carbon in rice paddy field of lombok island indonesia using near infrared technology. IOP Conference Series: Materials Science and Engineering, 306(1). https://doi.org/10.1088/1757-899X/306/1/012014
20. McGuirk S. L., Cairns I. H. (2024). Relationships between soil moisture and visible–NIR soil reflectance: A review presenting new analyses and data to fill the gaps. Geotechnics, 4(1), 78–108. https://doi.org/10.3390/geotechnics4010005
21. Makovníková J., Širáň M., Houšková B., Pálka B., Jones A. (2017). Comparison of different models for predicting soil bulk density. Case study – Slovakian agricultural soils. International Agrophysics, 31(4), 491–498. https://doi.org/10.1515/intag-2016-0079
22. Marion L. (1969). Soil chemical analysis: advanced course: a manual of methods useful for instruction and research in soil chemistry, physical chemistry of soils, soil fertility, and soil genesis. (available at http://www.sidalc.net/cgi-bin/wxis. exe/?IsisScript=bac.xis&method=post&formato= 2&cantidad=1&expresion=mfn=029021)
23. Marmette M., Adamchuk V., Nault J., Tabatabai S., Cocciardi R. (2018). Comparison of the Performance of Two Vis-NIR Spectrometers in the Prediction of Various Soil Properties. Proceedings of the 14th International Conference on Precision Agriculture, Montreal, Quebec, Canada, (June), 1–12. (available at https://www.ispag.org/proceedings/?a ction=abstract&id=5404&search=types)
24. Martens H., Naes T. (1989). Assessment, validation and choice of calibration method. Multivariate Calibration: 237–266.
25. Minu S., Shetty A. (2018). Prediction accuracy of soil organic carbon from ground based visible near-infrared reflectance spectroscopy. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-017-0744-0
26. O’Rourke S. M., Holden N. M. (2011). Optical sensing and chemometric analysis of soil organic carbon - a cost effective alternative to conventional laboratory methods? Soil Use and Management. https://doi.org/10.1111/j.14752743.2011.00337.x
27. Omran S. (2017). Rapid soil analyses using modern sensing technology: toward a more sustainable agriculture. In The Handbook of Environmental Chemistry, 41–53. https://doi.org/10.1007/698
28. Ostovari Y., Ghorbani-Dashtaki S., Bahrami H., Abbasi M., Dematte A. M., Arthur E., Panagos P. (2018). Towards prediction of soil erodibility, SOM and CaCO 3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran. Geoderma, 314, 102–112. https://doi.org/10.1016/j.geoderma.2017.11.014
29. Ouerghemmi W., Gomez C., Naceur S., Lagacherie P. (2011). Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces. Geoderma, 163(3–4), 227–237. https://doi.org/10.1016/j.geoderma.2011.04.019
30. Pinheiro É., Ceddia M., Clingensmith C., Grunwald S., Vasques G. (2017). Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the central amazon. Remote Sensing, 9(4).https://doi.org/10.3390/rs9040293
31. Roberts C., Workman J., Reeves J. 2004. Near-infrared spectroscopy in agriculture. Agronomy, 44, 1–14.
32. Rossel R., Behrens T. (2010). Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158(1–2), 46–54. https://doi.org/10.1016/j.geoderma.2009.12.025
33. Siirt M. B., Species I. P. (2016). Visible and near infrared spectroscopy techniques for determination of some physical and chemical properties in Kazova. Visible and Near Infrared Spectroscopy Techniques for Determination of Some Physical and Chemical Properties in Kazova Watershed. Advances in Environmental Biology, 10, 61–72.
34. St. Luce M., Ziadi N., Gagnon B., Karam A. (2017). Visible near infrared reflectance spectroscopy prediction of soil heavy metal concentrations in paper mill biosolid- and liming by-product-amended agricultural soils. Geoderma, 288, 23–36. https://doi.org/10.1016/j.geoderma.2016.10.037
35. Stenberg B., Viscarra Rossel R. A., Mouazen A. M., Wetterlind J. (2010). Visible and near infrared spectroscopy in soil science. Advances in Agronomy (107). https://doi.org/10.1016/S0065-2113(10)07005-7
36. Terra F. S., Demattê J., Viscarra Rossel R. A. (2015). Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis-NIR and mid-IR reflectance data. Geoderma, 255, 81–93. https://doi.org/10.1016/j.geoderma.2015.04.017
37. Viscarra Rossel R. A., Behrens T., Ben-Dor E., Brown D. J., Demattê J. A., Shepherd K. D., Shi Z., Stenberg B., Stevens A., Adamchuk V., Aïchi H., Barthès B. G., Bartholomeus H. M., Bayer A. D., Bernoux M., Böttcher K., Brodský L., Du C. W., Chappell A., Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198–230. https://doi.org/10.1016/j. earscirev.2016.01.012
38. Viscarra Rossel R. A., Walvoort D., McBratney A. B., Janik L. J., Skjemstad J. O. (2006). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1–2), 59– 75. https://doi.org/10.1016/j.geoderma.2005.03.007
39. Vitharana U., Mishra U., Mapa R. B. (2019). National soil organic carbon estimates can improve global estimates. Geoderma, 337, 55–64. https:// doi.org/10.1016/j.geoderma.2018.09.005
40. Walkley A. (1947). A critical examination of a rapid method for determining organic carbon in soils – effect of variations in digestion conditions and of inorganic soil constituents. Soil Science, 63, 251–264. https://doi.org/10.1097/00010694194704000-00001
41. Wenjun J., Zhou S., Jingyi H., Shuo L. (2014). In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE. https://doi.org/10.1371/journal.pone.0105708
42. Wight P., Allen L., Tyler D., Labbe, N., Rials T., Ashworth A. (2016). Comparison of near infrared reflectance spectroscopy with combustion and chemical methods for soil carbon measurements in agricultural soils. Communications in Soil Science and Plant Analysis, 47(6), 731–742. https://doi.org/10.1080/00103624.2016.1146750
43. Wold S., Sjöström M., Eriksson L. (2001). PLS-regression: a basic comp with MR. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
44. Xu D., Ma W., Chen S., Jiang Q., He K., Shi Z. (2018). Assessment of important soil properties related to Chinese Soil Taxonomy based on vis– NIR reflectance spectroscopy. Computers and Electronics in Agriculture, 144, 1–8. https://doi.org/10.1016/j.compag.2017.11.029
45. Xu S., Zhao Y., Wang M., Shi X. (2018). Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma, 310, 29–43. https://doi.org/10.1016/j.geoderma.2017.09.013

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-d800b38f-534b-4fae-b855-d90fa7255a6b