Soil quality analysis using modern statistics and NIR spectroscopy procedure

• Autorzy: Almanjahie I.M. , Ahmad I. , Elmezouar Z.C. , Laksaci A.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we combine the recent development on mathematical statistics with modern chemistry in order to provide a new approach for soil quality analysis. Precisely, from modern chemistry we use near-infrared reflectance (NIR) spectroscopy procedure as a fast, accurate and inexpensive tool to evaluate chemical properties. Based on the collected data, the relationship between soil quality variables is modeled by using the functional statistics, which allows for analyzing a data as a curve or an image. The used predictor models are functional classical regression (FCR), functional local linear regression (FLLR), functional relative error regression (FRER) and functional robust regression (FRR). We prove that the performance of these models is closely linked to the homogeneity of the data. Considering the Abisko soil data, we show that FNR and FLLR are suitable for soil organic matter data, while for the Ergosterol concentration data the use of FRER and FRR are adequate. Furthermore, the proposed functional approach, permits us to avoid many drawbacks of the classical approach as principal component regression (P.C.R.).

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2019
• Tom: 28
• Numer: 5
• Opis fizyczny: p.3581-3588,fig.,ref.

Twórcy

autor

Almanjahie I.M.

• Department of Mathematics, College of Science, King Khalid University, 61413, Abha, Kingdom of Saudi Arabia

autor

Ahmad I.

• Department of Mathematics, College of Science, King Khalid University, 61413, Abha, Kingdom of Saudi Arabia
• Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan

autor

Elmezouar Z.C.

• Department of Mathematics, College of Science, King Khalid University, 61413, Abha, Kingdom of Saudi Arabia

autor

Laksaci A.

• Department of Mathematics, College of Science, King Khalid University, 61413, Abha, Kingdom of Saudi Arabia

Bibliografia

• 1. MÖLLER K., OBERSON A., BÜNEMANN E.K., COOPER J., FRIEDEL J.K., GLAESNER N., HÖRTENHUBER S., LØES A.K., MÄDER P., MEYER G., MÜLLER T. Improved phosphorus recycling in organic farming: navigating between constraints. In: Advances in Agronomy. 147, 159, 2018.
• 2. WOLLMANN I., GAURO A., MÜLLER T., MÖLLER K. Phosphorus bioavailability of sewage sludge-based recycled fertilizers. Journal of Plant Nutrition and Soil Science. 181, 158, 2018.
• 3. TEN HOEVE M., BRUUN S., NAROZNOVA I., LEMMING C., MAGID J., JENSEN L.S., SCHEUTZ C. Life cycle inventory modeling of phosphorus substitution, losses and crop uptake after land application of organic waste products. The International Journal of Life Cycle Assessment, 45, 1, 2017.
• 4. RAYMOND N.S., STÖVER D.M., JENSEN L.S., HÅKANSSON S. Survival and phosphate solubilisation activity of desiccated formulations of Penicillium bilaiae and Aspergillus niger influenced by water activity. Journal of microbiological methods, 1, 150, 2018.
• 5. WEISSENGRUBER L., MÖLLER K., PUSCHENREITER M., FRIEDEL J.K. Long-term soil accumulation of potentially toxic elements and selected organic pollutants through application of recycled phosphorus fertilizers for organic farming conditions. Nutrient Cycling in Agroecosystems, 110, 427, 2018.
• 6. SMITH L.E., SICILIANO G. A comprehensive review of constraints to improved management of fertilizers in China and mitigation of diffuse water pollution from agriculture. Agriculture, Ecosystems & Environment, 209, 15, 2015.
• 7. HUANG J., XU C.C., RIDOUTT B.G., WANG X.C., REN P.A. Nitrogen and phosphorus losses and eutrophication potential associated with fertilizer application to cropland in China. Journal of Cleaner Production. 159, 171, 2017.
• 8. PANG J., CHEN X., ZHANG Z., LI H. Measuring eco-efficiency of agriculture in China. Sustainability. 8, 398, 2016.
• 9. LI J., RODRIGUEZ D., TANG X. Effects of land lease policy on changes in land use, mechanization and agricultural pollution. Land Use Policy, 64, 405, 2017.
• 10. PAN D., KONG F., ZHANG N., YING R. Knowledge training and the change of fertilizer use intensity: Evidence from wheat farmers in China. Journal of environmental management. 197, 130, 2017.
• 11. FOLEY W.J., MCILWEE A., LAWLER I., ARAGONES L., WOOLNOUGH A.P., BERDING N.,. Ecological applications of near infrared reflectance spectroscopy a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia 116, 293, 1998.
• 12. SUMMERS D., LEWIS M., OSTENDORF B., CHITTLEBOROUGH D. Visible NIRspectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11, 123, 2011.
• 13. ISLAM K., SINGH B., MCBRATNEY A. Simultaneous estimation of several soil properties by ultra-violet, visible, and NIRspectroscopy. Australian Journal of Soil Research, 41, 1101, 2003.
• 14. VELASQUEZ E., LAVELLE P., BARRIOS E., JOFFRE R., REVERSAT F., Evaluating soil quality in tropical agroecosystems of Colombia using NIRS. Soil Biology & BioChemistry 37, 889, 2005.
• 15. FRITZE H., JA¨RVINEN P., HIUKKA R.,. Near-infrared characteristics of forest humus are correlated with soil respiration and microbial biomass in burnt soil. Biology and Fertility of Soils 18, 80, 1994.
• 16. PIETIKAINEN J., FRITZE H. Clear-cutting and prescribed burning in coniferous forest-comparison of effects on soil fungal and total microbial biomass, respiration activity and nitrification. Soil Biology & BioChemistry 27, 101, 1995.
• 17. PLUCHON N., VINCENT A.G., GUNDALE M.J., NILSSON M.C., KARDOL P., WARDLE D.A. The impact of charcoal and soil mixtures on decomposition and soil microbial communities in boreal forest. Applied soil ecology, 99, 40, 2016.
• 18. BARDGETT R.D., DE VRIES F.T., VAN DER PUTTEN W.H. Soil Biodiversity and Ecosystem Functioning. In Microbial Biomass: A Paradigm Shift in Terrestrial Biogeo Chemistry, 45, 119, 2017.
• 19. CHODAK M., KHANNA P., BEESE F. Hot water extractable C and N in relation to microbiological properties of soils under beech forests. Biology and Fertility of Soils 39, 123, 2003.
• 20. LUDWIG B., KHANNA P.K., BAUHUS J., HOPMANS P. Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability. Forest Ecology and Management 171, 121, 2002.
• 21. CHODAK M., LUDWIG B., KHANNA P., BEESE F. Use of near infrared spectroscopy to determine biological and chemical characteristics of organic layers under spruce and beech stands. Journal of Plant Nutrition and Soil Science, 165, 27, 2002.
• 22. POST W.M., EMANUEL W.R., ZINKE P.J., STANGENBERGER A.G. Soil carbon pools and world life zones. Nature, 298, 156, 1982.
• 23. FERRATY F. High-dimensional data: a fascinating statistical challenge. Journal of Multivariate Analysis. 101, 305, 2010.
• 24. ANEIROS G., CAO R., FRAIMAN R., VIEU P. Special issue on functional data analysis and related topics. J. Multivariate Anal. (2018 to appear).
• 25. HAVSTRÖM M., CALLAGHAN T.V., JONASSON S. Differential growth responses of Cassiope tetragona, an arctic dwarf-shrub, to environmental perturbations among three contrasting high-and subarctic sites. Oikos. 389, 1993 .
• 26. RUMPF S.B., SEMENCHUK P.R., DULLINGER S., COOPER E.J. Idiosyncratic responses of high arctic plants to changing snow regimes. PLoS One. 11, 862, 2014.
• 27. XU Z., HU T., ZHANG Y. Effects of experimental warming on phenology, growth and gas exchange of treeline birch (Betula utilis) saplings, Eastern Tibetan Plateau, China. European Journal of Forest Research., 131, 811, 2012.
• 28. GORNALL J.L., WOODIN S.J., JÓNSDÓTTIR I.S., VAN DER WAL R. Balancing positive and negative plant interactions: how mosses structure vascular plant communities. Oecologia. 166, 769, 2011.
• 29. VAN DER WAL R., STIEN A. High- arctic plants like it hot: a long- term investigation of between- year variability in plant biomass. Ecology. 95, 341, 2014.
• 30. RINNAN R., MICHELSEN A., BÅÅTH E., JONASSON S. Fifteen years of climate change manipulations alter soil microbial communities in a subarctic heath ecosystem, Global Change Biology 13, 28, 2007.
• 31. FERRATY F., VIEU P. Nonparametric functional data analysis. Theory and Practice., Springer Series in Statistics. New York. 2006.
• 32. FERRATY F., VIEU P. Nonparametric functional data analysis: theory and practice. Springer Science & Business Media; 2006.
• 33. HORVÁTH L., KOKOSZKA P. Inference for functional data with applications. Springer Science & Business Media; 2012.
• 34. PESARIN F., SALMASO L. Permutation tests for complex data: theory, applications and software. John Wiley & Sons; 2010.
• 35. SUN Y., GENTON M.G. Functional boxplots. Journal of Computational and Graphical Statistics. 20, 316, 2011.
• 36. FEBRERO-BANDE M., DE LA FUENTE M.O. Statistical computing in functional data analysis: The R package fda. usc. Journal of Statistical Software. 51, 1, 2012.
• 37. VILAR J.M., CAO R., ANEIROS G. Forecasting nextday electricity demand and price using nonparametric functional methods. International Journal of Electrical Power & Energy Systems. 39, 48, 2012.
• 38. WANG J.L., CHIOU J.M., MÜLLER H.G. Functional data analysis. Annual Review of Statistics and Its Application. 3, 257, 2016.
• 39. FAN J., HECKMAN N.E., WAND M.P. Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. Journal of the American Statistical Association. 90, 141, 1995.
• 40. BARRIENTOS-MARIN J., FERRATY F., VIEU P. Locally modelled regression and functional data. Journal of Nonparametric Statistics. 22, 617, 2010.
• 41. GOIA A., VIEU P. An introduction to recent advances in high/infinite dimensional Statistics. 2016.
• 42. FERRATY F., LAKSACI A., TADJ A., VIEU P. Kernel regression with functional response. Electronic Journal of Statistics. 5, 159, 2011.
• 43. BERLINET A., ELAMINE A., MAS A. Local linear regression for functional data. Annals of the Institute of Statistical Mathematics. 63, 1047, 2011.
• 44. DEMONGEOT J., LAKSACI A., RACHDI M., RAHMANI S. On the local linear modelization of the conditional distribution for functional data. Sankhya A. 76, 328, 2014 .
• 45. DEMONGEOT J., HAMIE A., LAKSACI A., RACHDI M. Relative-error prediction in nonparametric functional Statistics: Theory and practice. Journal of Multivariate Analysis. 146, 261, 2016.
• 46. ALTENDJI B., DEMONGEOT J., LAKSACI A., RACHDI M. Functional data analysis: estimation of the relative error in functional regression under random left-truncation model. Journal of Nonparametric Statistics. 30, 472, 2018.
• 47. HAO M., LIN Y., ZHAO X. A relative error-based approach for variable selection. Computational Statistics & Data Analysis. 103, 250, 2016.
• 48. AZZEDINE N., LAKSACI A., OULD-SAÏD E. On robust nonparametric regression estimation for a functional regressor. Statistics & Probability Letters. 78, 3216, 2008.
• 49. ATTOUCH M.K., LAKSACI A., SAID EO. Robust regression for functional time series data. Journal of the Japan Statistical Society. 42, 125, 2013.
• 50. STONE C.J., Nonparametric M-regression with free knot splines. J. Statist. Planning and Inference, 130, 183, 2005.
• 51. RACHDI M., LAKSACI A., DEMONGEOT J., ABDALI A., MADANI F. Theoretical and practical aspects of the quadratic error in the local linear estimation of the conditional density for functional data. Computational Statistics & Data Analysis. 73, 53, 2014.
• 52. ATTOUCH M., LAKSACI A., OULD-SAID E. Asymptotic distribution of robust estimator for functional nonparametric models. Communications in Statistics – Theory and Methods. 38, 1317, 2009.

Identyfikatory

DOI

10.15244/pjoes/99110