Investigation of soil surface organic and inorganic carbon contents in a low-intensity farming system using laboratory visible and near-infrared spectroscopy

References

• Aïchi H, Fouad Y, Walter C, Viscarra Rossel RA, Lili Chabaane Z, Sanaa M. 2009. Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosyst Eng. 104(3):442–446. doi:10.1016/j.biosystemseng.2009.08.002.
   Web of Science ®Google Scholar
• Alberti G, Leronni V, Piazzi M, Petrella F, Mairota P, Peressotti A, Piussi P, Valentini R, Gristina L, Mantia TL, et al. 2011. Impact of woody encroachment on soil organic carbon and nitrogen in abandoned agricultural lands along a rainfall gradient in Italy. Reg Environ Chang. 11(4):917–924. doi:10.1007/s10113-011-0229-6.
   Web of Science ®Google Scholar
• Andersen CM, Bro R. 2010. Variable selection in regression-a tutorial. J Chemom. 24(11–12):728–737. doi:10.1002/cem.1360.
   Web of Science ®Google Scholar
• Autret B, Mary B, Chenu C, Balabane M, Girardin C, Bertrand M, Grandeau G, Beaudoin N. 2016. Alternative arable cropping systems: A key to increase soil organic carbon storage? Results from a 16 year field experiment. Agric Ecosyst Environ. 232:150–164. doi:10.1016/j.agee.2016.07.008.
   Web of Science ®Google Scholar
• Baldock JA, Skjemstad JO. 2000. Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Org Geochem. 31(7):697–710. doi:10.1016/S0146-6380(00)00049-8.
   Web of Science ®Google Scholar
• Baumgardner MF, Silva LRF, Biehl LL, Stoner ER. 1986. Reflectance properties of soils. Adv Agron. 38:1–44.
   Web of Science ®Google Scholar
• Bellon-Maurel V, Fernandez-Ahumada E, Roger BPJM, McBratney A. 2010. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. Trends Anal Chem. 29:1073–1081. doi:10.1016/j.trac.2010.05.006.
   Web of Science ®Google Scholar
• Bellon-Maurel V, McBratney A. 2011. Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils–critical review and research perspectives. Soil Biol Biochem. 43:1398–1410. doi:10.1016/j.soilbio.2011.02.019.
   Web of Science ®Google Scholar
• Ben Dor E, Levin N, Singer A, Karnieli A, Braun O, Kidron GJ. 2006. Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor. Geoderma 131(1–2):1–21. doi:10.1016/j.geoderma.2005.02.011.
   Web of Science ®Google Scholar
• Castrignanò A, Buttafuoco G, Quarto R, Parisi D, Viscarra Rossel RA, Terribile F, Langella G, Venezia A. 2018. A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. Catena 167:293–304. doi:10.1016/j.catena.2018.05.011.
   Web of Science ®Google Scholar
• Chilès JP, Delfiner P. 2012. Geostatistics: modeling spatial uncertainty. 2nd ed. Hoboken (NJ): Wiley. Wiley Series in Probability and Statistics.
   Google Scholar
• Colombo C, Palumbo G, Di Iorio E, Sellitto VM, Comolli R, Stellacci AM, Castrignanò A. 2014. Soil organic carbon variation in alpine landscape (Northern Italy) as evaluated by diffuse reflectance spectroscopy. Soil Sci Soc Am J. 78:794–804. doi:10.2136/sssaj2013.11.0488.
   Web of Science ®Google Scholar
• Conforti M, Buttafuoco G, Leone A, Aucelli PPC, Robustelli G, Scarciglia F. 2013b. Studying the relationship between water-induced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis: a case study in a southern Italy area. Catena 110:44-–58. doi:10.1016/j.catena.2013.06.013.
   Web of Science ®Google Scholar
• Conforti M, Froio R, Matteucci G, Caloiero T, Buttafuoco G. 2013a. Potentiality of laboratory visible and near infrared spectroscopy for determining clay content in forest soils: a case study from high forest beech (fagus sylvatica) in Calabria (southern Italy). Intern J Environ Quality 11:49–64.
   Google Scholar
• Conforti M, Matteucci G, Buttafuoco G. 2018. Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties. J Soils Sediments. 18:1009–1019. doi:10.1007/s11368-017-1766-5.
   Web of Science ®Google Scholar
• Curran PJ. 1994. Imaging Spectrometry. Progr Phys Geogr: Earth Envir. 18:247–266. doi:10.1177/030913339401800204.
   Google Scholar
• Farifteh J, van der Meer F, Atzberger C, Carranza EJM. 2007. Quantitative analysis of salt-affected soil re-flectance spectra: A comparison of two adaptive methods (PLSR and ANN). Remote Sens Environ. 110:59–78. doi:10.1016/j.rse.2007.02.005.
   Web of Science ®Google Scholar
• Gaffey MJ, Bell JF, Brown RH, Burbine TH, Piatek JL, Reed KL, Chaky DA. 1993. Mineralogical variations within the S-Type Asteroid class. Icarus. 106:573–602. doi:10.1006/icar.1993.1194.
   Web of Science ®Google Scholar
• Ge Y, Morgan CLS, Thomasson JA, Waiser T. 2007. A new perspective to near-infrared reflectance spectroscopy: A wavelet approach. Am Soc Agric Biol Eng. 50:303–311.
   Web of Science ®Google Scholar
• Goddu RF, Delker DA. 1960. Spectra-structure correlations for the near-infrared. Region Anal Chem. 32:140–141.
   Web of Science ®Google Scholar
• Gulde S, Chung H, Amelung W, Chang C, Six J. 2008. Soil carbon saturation controls labile and stable carbon pool dynamics. Soil Sci Soc Am J. 72:605–612. doi:10.2136/sssaj2007.0251.
   Web of Science ®Google Scholar
• IUSS Working Group WRB. 2015. World Reference Base for Soil Resources 2014, Update 2015 International soil classification system for naming soils and creating legends for soil maps. Rome (Italy): World Soil Resources Reports No. 106. Food and Agriculture Organization of the United Nations.doi:10.1017/S0014479706394902.
   Google Scholar
• Knox NM, Grunwald S, McDowell ML, Bruland GL, Myers DB, Harris WG. 2015. Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239–240:229–239. doi:10.1016/j.geoderma.2014.10.019.
   Web of Science ®Google Scholar
• Lagacherie P, Baret F, Feret JB, Madeira Netto J, Robbez-Masson JM. 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sens Environ. 112:825–835. doi:10.1016/j.rse.2007.06.014.
   Web of Science ®Google Scholar
• Lal R, Kimble JM. 2000. Pedogenic carbonates and the global carbon cycle. In: Lal R, Kimble JM, Eswaran H, Stewart BA, editors. Global climate change and pedogenic carbonates. Boca Raton (FL): CRC Press; p. 1–14.
   Google Scholar
• Martens H, Næs T. 1989. Multivariate calibration. Chichester (UK): Wiley.
   Google Scholar
• Mikutta R, Kleber M, Torn MS, Jahn R. 2006. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 77:25–56. doi:10.1007/s10533-005-0712-6.
   Web of Science ®Google Scholar
• Næs T, Isakson T, Fearn T, Davies T. 2002. A user-Friendly guide to Multivariate Calibration and Classification. Chichester (UK): NIR Publications.
   Google Scholar
• Nanni MR, Demattè JAM. 2006. Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis. Soil Sci Soc Am J. 70:393–407. doi:10.2136/sssaj2003.0285.
   Web of Science ®Google Scholar
• Rienzi EA, Mijatovic B, Mueller TG, Matocha CJ, Sikora FJ, Castrignanò A. 2014. Prediction of soil organic carbon under varying moisture levels using reflectance spectroscopy. Soil Sci Soc Am J. 78:958–967. doi:10.2136/sssaj2013.09.0408.
   Web of Science ®Google Scholar
• SAS Institute Inc. 2017. SAS/STAT software, release 9.4. Cary:SAS Institute Inc.
   Google Scholar
• Savitzky A, Golay MJE. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 36:1627–1639. doi:10.1021/ac60214a047.
   Web of Science ®Google Scholar
• Scharpenseel HW, Mtimet A, Freytag J. 2000. Soil inorganic carbon and global change. In: Lal R, Kimble JM, Eswaran H, Stewart BA, editors. Global climate and pedogenic carbonates. Boca Raton (FL): CRC Press; p. 27–45.
   Google Scholar
• Seibert J, Stendahl J, Sørensen R. 2007. Topographical influences on soil properties in boreal forests. Geoderma 141:139–148. doi:10.1016/j.geoderma.2007.05.013.
   Web of Science ®Google Scholar
• Shepherd KD, Walsh MG. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J. 66:988–998. doi:10.2136/sssaj2002.9880.
   Web of Science ®Google Scholar
• Siesler HW, Ozaki Y, Kawata S, Heise HM, editors. 2002. Near-infrared spectroscopy: principles, instruments, applications. 1st ed. Weinheim (G): Wiley–VCH.
   Google Scholar
• Skjemstad JO, Dalal RC. 1987. Spectroscopic and chemical differences in organic matter of two vertisols subjected to long periods of cultivation. Aust J Soil Res. 25:323–335. doi:10.1071/SR9870323.
   Web of Science ®Google Scholar
• Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J. 2010. Visible and near infrared spectroscopy in soil science. Adv Agron. 107:163–215.
   Web of Science ®Google Scholar
• Stevens A, van Wesemael B, Bartholomeus H, Rosillon D, Tychon B, Ben-Dor E. 2008. Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma 144:395–404. doi:10.1016/j.geoderma.2007.12.009.
   Web of Science ®Google Scholar
• Tan Z, Lal R. 2005. Carbon sequestration potential estimates with changes in land use and tillage practice in Ohio, USA. Agric Ecosyst Environ. 111:140–152. doi:10.1016/j.agee.2005.05.012.
   Web of Science ®Google Scholar
• Van der Voet H. 1994. Comparing the predictive accuracy of models using a simple randomization test. Chemom Intell Lab Syst. 25:313–323. doi:10.1016/0169-7439(94)85050-X.
   Web of Science ®Google Scholar
• Viscarra Rossel RA. 2008. ParLeS: software for chemometric analysis of spectroscopic data. Chemom Intell Lab Syst. 90:72–83. doi:10.1016/j.chemolab.2007.06.006.
   Web of Science ®Google Scholar
• Viscarra Rossel RA, Behrens T. 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54. doi:10.1016/j.geoderma.2009.12.025.
   Web of Science ®Google Scholar
• Viscarra Rossel RA, Hicks WS. 2015. Soil organic carbon and its fractions estimated by visible-near infrared transfer functions. Eur J Soil Sci. 66:438–450. doi:10.1111/ejss.2015.66.issue-3.
   Web of Science ®Google Scholar
• Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO. 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131:59–75. doi:10.1016/j.geoderma.2005.03.007.
   Web of Science ®Google Scholar
• Wackernagel H. 2003. Multivariate geostatistics. An introduction with applications. 3rd ed. Berlin (G): Springer.
   Google Scholar
• Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst. 58:109–130. doi:10.1016/S0169-7439(01)00155-1.
   Web of Science ®Google Scholar
• Workman JJ, Weyer L. 2007. Practical guide to interpretive near-infrared spectroscopy. 1st ed. Boca Raton (FL): CRC Press.
   Google Scholar
• Workman JJ, Weyer L. 2008. Practical Guide to Interpretive Near-Infrared Spectroscopy. 2nd ed. Boca Raton (FL): CRC Press.
   Google Scholar
• Xie J, Li Y, Zhai C, Li C, Lan Z. 2009. CO2 absorption by alkaline soils and its implication to the global carbon cycle. Environ Geol. 56:953–961. doi:10.1007/s00254-008-1197-0.
   Web of Science ®Google Scholar