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Research Article

Exploring laboratory-based spectroscopy for estimating NPK content in the hutton soils of Syferkuil Farmlands, South Africa

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Article: 2339289 | Received 05 Dec 2023, Accepted 01 Apr 2024, Published online: 10 Apr 2024

References

  • Babyak MA. 2004. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 66(3):411–421. doi: 10.1097/01.psy.0000127692.23278.a9.
  • Banerjee S, van der Heijden MG. 2023. Soil microbiomes and one health. Nat Rev Microbiol. 21(1):6–20. doi: 10.1038/s41579-022-00779-w.
  • Bangelesa F, Adam E, Knight J, Dhau I, Ramudzuli M, Mokotjomela TM. 2020. Predicting soil organic carbon content using hyperspectral remote sensing in a degraded mountain landscape in lesotho. Appl Environ Soil Sci. 2020:1–11. doi: 10.1155/2020/2158573.
  • Chagas JO, Gomes JM, Rodrigues JHV, de Matos Cunha IC, De Melo NFS, Da Silva GA, Lobo FA. 2021. Nitrogen, phosphorous, and potassium polymeric microparticles: application and validation of analytical methods for determination of a promising fertilizer. Eclética Química. 46(2):36–47.
  • Cheng H, Shen R, Chen Y, Wan Q, Shi T, Wang J, Wan Y, Hong Y, Li X. 2019. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma. 336:59–67. doi: 10.1016/j.geoderma.2018.08.010.
  • Chen J, Gu S, Shen M, Tang Y, Matsushita B. 2009. Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data. Int J Remote Sens. 30(24):6497–6517. doi: 10.1080/01431160902882496.
  • Cohen I, Huang Y, Chen J, Benesty J. 2009. Pearson correlation coefficient. Noise Reduction in Speech Process. 2:1–4. doi: 10.1007/978-3-642-00296-0_5.
  • Corona MEP, Aldana BRVD, Criado BG, Ciudad AG. 1998. Variations in Nutritional Quality and Biomass Production of Semiarid Grasslands. Journal of Range Management. 51(5):570.doi: 10.2307/4003378.
  • Correndo AA, Rubio G, García FO, Ciampitti IA. 2021. Subsoil-potassium depletion accounts for the nutrient budget in high-potassium agricultural soils. Sci Rep. 11(1):11597. doi: 10.1038/s41598-021-90297-1.
  • El-Sayed MA, Abd-Elazem AH, Moursy AR, Mohamed ES, Kucher DE, Fadl ME. 2023. Integration Vis-NIR spectroscopy and artificial intelligence to predict some soil parameters in arid region: a case study of Wadi Elkobaneyya, South Egypt. Agronomy. 13(3):935. doi: 10.3390/agronomy13030935.
  • Ewing J, Oommen T, Jayakumar P, Alger R. 2020. Utilizing hyperspectral remote sensing for soil gradation. Remote Sens. 12(20):3312. doi: 10.3390/rs12203312.
  • Farrar MB, Wallace HM, Tahmasbian I, Yule CM, Dunn PK, Bai SH. 2023. Rapid assessment of soil carbon and nutrients following application of organic amendments. Catena. 223:106928. doi: 10.1016/j.catena.2023.106928.
  • Farrés M, Platikanov S, Tsakovski S, Tauler R. 2015. Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. J Chemom. 29(10):528–536. doi: 10.1002/cem.2736.
  • Fystro G. 2002. The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods. Plant and Soil. 246(2):139–149. doi: 10.1023/A:1020612319014.
  • Gower JC, Le Roux NJ, Gardner−Lubbe S. 2016. Biplots: qualititative data. WIREs Computational Stats. 8(2):82–111. doi: 10.1002/wics.1377.
  • Guo P, Li T, Gao H, Chen X, Cui Y, Huang Y. 2021. Evaluating calibration and spectral variable selection methods for predicting three soil nutrients using vis-nir spectroscopy. Remote Sens. 13(19):4000. doi: 10.3390/rs1319:4000.
  • Hively WD, McCarty GW, Reeves JB, Lang MW, Oesterling RA, Delwiche SR. 2011. Use of airborne hyperspectral imagery to map soil properties in tilled agricultural fields. Appl Environ Soil Sci. 2011:1–13. doi: 10.1155/2011/358193.
  • Jin X, Li S, Zhang W, Zhu J, Sun J. 2020. Prediction of soil-available potassium content with visible near-infrared ray spectroscopy of different pretreatment transformations by the boosting algorithms. Applied Sciences (Switzerland). 10(4):1520. doi: 10.3390/app10041520.
  • Johnston R, Jones K, Manley D. 2018. Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Qual Quant. 52(4):1957–1976. doi: 10.1007/s11135-017-0584-6.
  • Kim MJ, Lee JE, Back I, Lim KJ, Mo C. 2023. Estimation of total nitrogen content in topsoil based on machine and deep learning using hyperspectral imaging. Agriculture. 13(10):1975. doi: 10.3390/agriculture13101975.
  • Kokaly RF, Clark RN. 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ. 67(3):267–287. doi: 10.1016/S0034-4257(98)00084-4.
  • Lin L, Gao Z, Liu X. 2020. Estimation of soil total nitrogen using the synthetic color learning machine (SCLM) method and hyperspectral data. Geoderma. 380:114664. doi: 10.1016/j.geoderma.2020.114664.
  • Lumbanraja J, Mulyani S, Utomo M, Sarno S. 2017. Phosphorus extraction from soil constituents using Bray P-1, Mehlich-1 and Olsen Solutions. J Trop Soils. 22(2):67–76. doi: 10.5400/jts.2017.v22i2.67-76.
  • Malmir M, Tahmasbian I, Xu Z, Farrar MB, Bai SH. 2019. Prediction of soil macro-and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique. Geoderma. 340:70–80. doi: 10.1016/j.geoderma.2018.12.049.
  • Masrie M, Rosman MSA, Sam R, Janin Z. 2017. Detection of nitrogen, phosphorus, and potassium (NPK) nutrients of soil using optical transducer. In 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). IEEE. doi: 10.1109/ICSIMA.2017.8312001.
  • Massey Jr FJ. 1951. The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc. 46(253):68–78. doi: 10.1080/01621459.1951.10500769.
  • McKnight PE, Najab J. 2010. Mann−Whitney U Test. The Corsini Encyclopedia of Psychol. 1:1–1. doi: 10.1002/9780470479216.corpsy0491.
  • Moshia ME, Mashatola MB, Shaker P, Fouché PS, Boshomane MAW. 2008. Land suitability assessment and precision farming prospects for irrigated maize-soybean intercropping in syferkuil experimental farm using geospatial information technology. J Agric Soc Res. 8(2):1–12. doi: 10.4314/jasr.v8i2.43351.
  • Munjonji L, Ayisi KK, Vandewalle B, Dhau I, Boeckx P, Haesaert G. 2017. Yield performance, carbon assimilation and spectral response of triticale to water stress. Ex Agric. 53(1):100–117. doi: 10.1017/S0014479716000107.
  • Munjonji L, Ayisi KK, Mudongo EI, Mafeo TP, Behn K, Mokoka MV, Linstädter A. 2020. Disentangling drought and grazing effects on soil carbon stocks and CO2 fluxes in a semi-arid African savanna. Front Environ Sci. 8:1–14. doi: 10.3389/fenvs.2020.590665.
  • Nettleton D. 2014. Pearson correlation-an overview. Science Direct Topics. Available at: https://www.sciencedirect.com/topics/computer-science/pearson-correlation (Accessed 19 Septermber 2023).
  • Nguyen KA, Liou YA, Tran HP, Hoang PP, Nguyen TH. 2020. Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Prog Earth Planet Sci. 7(1):1–16. doi: 10.1186/s40645-019-0311-0.
  • Pantazi XE, Moshou D, Bravo C. 2016. Active learning system for weed species recognition based on hyperspectral sensing. Biosyst Eng. 146:193–202. doi: 10.1016/j.biosystemseng.2016.01.014.
  • Patel AK, Ghosh JK. 2019. Soil fertility status assessment using hyperspectral remote sensing. SPIE-Intl Soc Optical Eng. 14. doi: 10.1117/12.2533115.
  • Peng Y, Wang L, Zhao L, Liu Z, Lin C, Hu Y, Liu L. 2021. Estimation of soil nutrient content using hyperspectral data. Agriculture. 11(11):1129. doi: 10.3390/agriculture11111129.
  • Peng Y, Zhao L, Hu Y, Wang G, Wang L, Liu Z. 2019. Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy. IJGI. 8(10):437. doi: 10.3390/ijgi8100437.
  • Phefadu KC, Kutu FR. 2016. Evaluation of spatial variability of soil physico-chemical characteristics on rhodic ferralsol at the syferkuil experimental farm of University of Limpopo, South Africa. JAS. 8(10):92. doi: 10.5539/jas.v8n10p92.
  • Qi H, Paz-Kagan T, Karnieli A, Li S. 2017. Linear multi-task learning for predicting soil properties using field spectroscopy. Remote Sens. 9(11):1099. doi: 10.3390/rs9111099.
  • Salehi-Varnousfaderani B, Honarbakhsh A, Tahmoures M, Akbari M. 2022. Soil erodibility prediction by Vis-NIR spectra and environmental covariates coupled with GIS, regression and PLSR in a watershed scale, Iran. Geoderma Regional. 28:e00470. doi: 10.1016/j.geodrs.2021.e00470.
  • Savitzky A, Golay MJ. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 36(8):1627–1639. doi: 10.1021/ac60214a047.
  • Selige T, Böhner J, Schmidhalter U. 2006. High-resolution topsoil mapping using hyperspectral image and field data in multivariate regression modelling procedures. Geoderma. 136(1-2):235–244. doi: 10.1016/j.geoderma.2006.03.050.
  • Sharifi M, Zebarth BJ, Burton DL, Grant CA, Hajabbasi MA, Abbassi−Kalo G. 2009. Sodium hydroxide direct distillation: A method for estimating total nitrogen in soil. Commun Soil Sci Plant Anal. 40(15–16):2505–2520. doi: 10.1080/00103620903111376.
  • Shi T, Cui L, Wang J, Fei T, Chen Y, Wu G. 2013. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil. 366(1–2):363–375. doi: 10.1007/s11104-012-1436-8.
  • Smith ML, Martin ME, Plourde L, Ollinger SV. 2003. Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor. IEEE Trans Geosci Remote Sensing. 41(6):1332–1337. doi: 10.1109/TGRS.2003.813128.
  • Song X, Gao Y, Liu Z, Zhang M, Wan Y, Yu X, Liu W, Li L. 2019. Development of a predictive tool for rapid assessment of soil total nitrogen in wheat-corn double cropping system with hyperspectral data. Environ Pollutants and Bioavail. 31(1):272–281. doi: 10.1080/26395940.2019.1679041.
  • Song YQ, Zhao X, Su HY, Li B, Hu YM, Cui X. 2018. Predicting spatial variations in soil nutrients with hyperspectral remote sensing at regional scale. Sensors (Basel). 18(9):3086. doi: 10.3390/s18093086.
  • Soriano-Disla JM, Janik LJ, Viscarra Rossel RA, Macdonald LM, McLaughlin MJ. 2014. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl Spectrosc Rev. 49(2):139–186. doi: 10.1080/05704928.2013.811081.
  • Tavakoli H, Correa J, Sabetizade M, Vogel S. 2023. Predicting key soil properties from Vis-NIR spectra by applying dual-wavelength indices transformations and stacking machine learning approaches. Soil Tillage Res. 229:105684. doi: 10.1016/j.still.2023.105684.
  • Tavares TR, de Almeida E, Junior CRP, Guerrero A, Fiorio PR, de Carvalho HWP. 2023. Analysis of total soil nutrient content with X-ray Fluorescence Spectroscopy (XRF): Assessing different predictive modeling strategies and auxiliary variables. AgriEngineering. 5(2):680–697. doi: 10.3390/agriengineering5020043.
  • Vibhute AD, Kale KV, Gaikwad SV, Dhumal RK. 2020. Estimation of soil nitrogen in agricultural regions by VNIR reflectance spectroscopy. SN Appl Sci. 2(9):1–8. doi: 10.1007/s42452-020-03322-9.
  • Viscarra Rossel RA, Lark RM. 2009. Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. European J Soil Science. 60(3):453–464. doi: 10.1111/j.1365-2389.2009.01121.x.
  • Vohland M, Ludwig B, Seidel M, Hutengs C. 2022. Quantification of soil organic carbon at regional scale: benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches. Geoderma. 405:115426. doi: 10.1016/j.geoderma.2021.115426.
  • Xu Z, Chen S, Zhu B, Chen L, Ye Y, Lu P. 2022. Evaluating the capability of satellite hyperspectral imager, the ZY1–02D, for topsoil nitrogen content estimation and mapping of farmlands in black soil area, China. Remote Sens. 14(4):1008. doi: 10.3390/rs14041008.
  • Yu H, Kong B, Wang Q, Liu X, Liu X. 2020. Hyperspectral remote sensing applications in soil: a review. In: Hyperspectral remote sensing. Amsterdam, Netherlands: Elsevier; p. 269–291. doi: 10.1016/b978-0-08-102894-0.00011-5.
  • Zahir S, Jamlos MF, Omar AF, Jamlos MA, Mamat R, Muncan J, Tsenkova R. 2023. Review–Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor. Spectrochim Acta A Mol Biomol Spectrosc. 304:123273. doi: 10.1016/j.saa.2023.123273.
  • Zhai Y, Cui L, Zhou X, Gao Y, Fei T, Gao W. 2013. Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods. Int J Remote Sens. 34(7):2502–2518. doi: 10.1080/01431161.2012.746484.
  • Zribi M, Kotti F, Lili-Chabaane Z, Baghdadi N, Issa NB, Amri R. 2012. Analysis of soil texture using TERRASAR X-band SAR. In 2012 IEEE International Geoscience and Remote Sensing Symposium. p. 7027–7030.