Skip to main content

Advertisement

SpringerLink
Log in

Characteristics and Non-parametric Multivariate Data Mining Analysis and Comparison of Extensively Diversified Animal Manure

  • Original Paper
  • Published:
Waste and Biomass Valorization Aims and scope Submit manuscript

Abstract

Purpose

This study compared characteristics of different animal manure and examined non-parametric multivariate analysis tools’ suitability for their data mining. This can provide data and methodology support for scientific research and utilization of animal manure raw materials’ characteristics.

Methods

Distribution profile testing, statistical calculation, and Spearman correlation analysis—using characteristics of 788 animal manure samples of layer, broiler, pig, dairy, and beef, with fertilizer nutrient compositions, proximate compositions, ultimate compositions, and calorific values—were conducted. Latent associations between different animal manure types’ characteristics were examined through five non-parametric multivariate analyses.

Results

All samples’ physicochemical characteristics samples showed different non-normal distributions except potassium. Volatile matter (VM), fixed carbon (FC), ash, carbon, hydrogen, oxygen, and higher/lower heating value (HHV/LHV) were correlated, and nitrogen was positively correlated with phosphorus, potassium, and sulfur. Non-parametric principal component analysis (PCA), non-parametric exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), and non-metric multidimensional scaling (NMDS) obtained similar results: VM, FC, carbon, hydrogen, oxygen, HHV, and LHV had associated attributes (“energy utilization”); phosphorus, potassium, ash, nitrogen, and sulfur had intrinsic associated attributes (“fertilizer utilization”).

Conclusions

Animal manure characteristics should be mined and analyzed using non-parametric statistical analysis methods. Non-parametric PCA, non-parametric EFA, HCA, and NMDS are suitable for this purpose.

Graphic Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Not applicable.

Abbreviations

P:

Phosphorus

K:

Potassium

VM:

Volatile matter

FC:

Fixed carbon

C:

Carbon

H:

Hydrogen

N:

Nitrogen

S:

Sulfur

O:

Oxygen

HHV:

Higher heating values

LHV:

Lower heating value

SD:

Standard deviation

MAD:

Median absolute deviation

IQR:

Interquartile range

PCA:

Principal component analysis

EFA:

Exploratory factor analysis

HCA:

Hierarchical cluster analysis

NMDS:

Non-metric multidimensional scaling

DCA:

Detrended correspondence analysis

References

  1. MOARA: accelerate the disposal and recycling of animal breeding wastes. https://www.moa.gov.cn/xw/zwdt/201701/t20170112_5430265.htm (2017). Accessed 12 Jan 2017

  2. Ge, J.Y., Huang, G.Q., Li, J.B., Sun, X.X., Han, L.J.: Multivariate and multiscale approaches for interpreting the mechanisms of nitrous oxide emission during pig manure-wheat straw aerobic composting. Environ. Sci. Technol. 52(15), 8408–8418 (2018). https://doi.org/10.1021/acs.est.8b02958

    Article  Google Scholar 

  3. Cao, X.D., Ma, L.N., Gao, B., Harris, W.: Dairy-manure derived biochar effectively sorbs lead and atrazine. Environ. Sci. Technol. 43(9), 3285–3291 (2009). https://doi.org/10.1021/es803092k

    Article  Google Scholar 

  4. Husfeldt, A.W., Endres, M.I.: Association between stall surface and some animal welfare measurements in freestall dairy herds using recycled manure solids for bedding. J. Dairy. Sci. 95(10), 5626–5634 (2012). https://doi.org/10.3168/jds.2011-5075

    Article  Google Scholar 

  5. Poschl, M., Ward, S., Owende, P.: Evaluation of energy efficiency of various biogas production and utilization pathways. Appl. Energy 87(11), 3305–3321 (2010). https://doi.org/10.1016/j.apenergy.2010.05.011

    Article  Google Scholar 

  6. Li, J., Peng, S.: Practical Manual for Composting Engineering. Chemical Industry Press, Beijing (2018)

    Google Scholar 

  7. Liu, R., Niu, W., Zhang, D.: Biomass Thermochemical Conversion Technology. Renewable Energy. Chemical Industry Press, Beijing (2005)

    Google Scholar 

  8. Li, L., Yang, M., Lu, Q., Zhu, W.K., Ma, H.Q., Dai, L.C.: Oxygen-rich biochar from torrefaction: a versatile adsorbent for water pollution control. Bioresource Technol. 294, 12 (2019). https://doi.org/10.1016/j.biortech.2019.122142

    Article  Google Scholar 

  9. Chen, Z.M., Xiao, X., Chen, B.L., Zhu, L.Z.: Quantification of chemical states, dissociation constants and contents of oxygen-containing groups on the surface of biochars produced at different temperatures. Environ. Sci. Technol. 49(1), 309–317 (2015). https://doi.org/10.1021/es5043468

    Article  Google Scholar 

  10. Li, Y., Alaimo, C.P., Kim, M., Kado, N.Y., Peppers, J., Xue, J., Wan, C., Green, P.G., Zhang, R.H., Jenkins, B.M., Vogel, C.F.A., Wuertz, S., Young, T.M., Kleeman, M.J.: Composition and toxicity of biogas produced from different feedstocks in California. Environ. Sci. Technol. 53(19), 11569–11579 (2019). https://doi.org/10.1021/acs.est.9b03003

    Article  Google Scholar 

  11. Trabue, S.L., Kerr, B.J., Scoggin, K.D.: Swine diets impact manure characteristics and gas emissions: part II sulfur source. Sci. Total Environ. 689, 1115–1124 (2019). https://doi.org/10.1016/j.scitotenv.2019.06.272

    Article  Google Scholar 

  12. Trabue, S.L., Kerr, B.J., Scoggin, K.D.: Swine diets impact manure characteristics and gas emissions: part I sulfur level. Sci. Total Environ. 687, 800–807 (2019). https://doi.org/10.1016/j.scitotenv.2019.06.130

    Article  Google Scholar 

  13. Shen, X.L., Huang, G.Q., Yang, Z.L., Han, L.J.: Compositional characteristics and energy potential of Chinese animal manure by type and as a whole. Appl. Energy 160, 108–119 (2015). https://doi.org/10.1016/j.apenergy.2015.09.034

    Article  Google Scholar 

  14. Suresh, A., Choi, H.L., Oh, D.I., Moon, O.K.: Prediction of the nutrients value and biochemical characteristics of swine slurry by measurement of EC - Electrical conductivity. Bioresource Technol. 100(20), 4683–4689 (2009). https://doi.org/10.1016/j.biortech.2009.05.006

    Article  Google Scholar 

  15. Huang, G.Q., Wang, X.Y., Han, L.J.: Rapid estimation of nutrients in chicken manure during plant-field composting using physicochemical properties. Bioresource Technol. 102(2), 1455–1461 (2011). https://doi.org/10.1016/j.biortech.2010.09.086

    Article  Google Scholar 

  16. Han, L., Hu, Z., Yan, Q., Yi, L.: Correlations between chemical-physical properties and nutrient contents in broiler manure. Trans. Chin. Soc. Agric. Eng. 18(6), 123–126 (2002). https://doi.org/10.1007/s11769-002-0041-9

    Article  Google Scholar 

  17. Choi, H.L., Sudiarto, S.I.A., Renggaman, A.: Prediction of livestock manure and mixture higher heating value based on fundamental analysis. Fuel 116, 772–780 (2014). https://doi.org/10.1016/j.fuel.2013.08.064

    Article  Google Scholar 

  18. Wang, M.M., Cao, W.X., Sun, C., Sun, Z.X., Miao, Y., Liu, M.M., Zhang, Z.N., Xie, Y., Wang, X.X., Hu, S.T., Hu, C.W., Liu, R.H.: To distinguish the primary characteristics of agro-waste biomass by the principal component analysis: an investigation in East China. Waste Manage 90, 100–120 (2019). https://doi.org/10.1016/j.wasman.2019.04.046

    Article  Google Scholar 

  19. Moral, R., Perez-Murcia, M.D., Perez-Espinosa, A., Moreno-Casells, J., Paredes, C.: Estimation of nutrient values of pig slurries in Southeast Spain using easily determined properties. Waste Manage 25(7), 719–725 (2005). https://doi.org/10.1016/j.wasman.2004.09.010

    Article  Google Scholar 

  20. Dahlstrom, O., Zetterqvist, M., Lundh, L.G., Svedin, C.G.: Functions of nonsuicidal self-injury: exploratory and confirmatory factor analyses in a large community sample of adolescents. Psychol. Assess. 27(1), 302–313 (2015). https://doi.org/10.1037/pas0000034

    Article  Google Scholar 

  21. Bruelheide, H., Bohnke, M., Both, S., Fang, T., Assmann, T., Baruffol, M., Bauhus, J., Buscot, F., Chen, X.Y., Ding, B.Y., Durka, W., Erfmeier, A., Fischer, M., Geissler, C., Guo, D.L., Guo, L.D., Hardtle, W., He, J.S., Hector, A., Krober, W., Kuhn, P., Lang, A.C., Nadrowski, K., Pei, K.Q., Scherer-Lorenzen, M., Shi, X.Z., Scholten, T., Schuldt, A., Trogisch, S., von Oheimb, G., Welk, E., Wirth, C., Wu, Y.T., Yang, X.F., Zeng, X.Q., Zhang, S.R., Zhou, H.Z., Ma, K.P., Schmid, B.: Community assembly during secondary forest succession in a Chinese subtropical forest. Ecol. Monogr. 81(1), 25–41 (2011). https://doi.org/10.1890/09-2172.1

    Article  Google Scholar 

  22. Ahmad, Z., Khan, S.M., Ali, M.I., Fatima, N., Ali, S.: Pollution indicandum and marble waste polluted ecosystem; role of selected indicator plants in phytoremediation and determination of pollution zones. J. Clean Prod. (2019). https://doi.org/10.1016/j.jclepro.2019.117709

    Article  Google Scholar 

  23. Zhang, H., Liu, T., Zhang, Z., Payne, S.H., Zhang, B., McDermott, J.E., Zhou, J.Y., Petyuk, V.A., Chen, L., Ray, D., Sun, S.S., Yang, F., Chen, L.J., Wang, J., Shah, P., Cha, S.W., Aiyetan, P., Woo, S., Tian, Y., Gritsenko, M.A., Clauss, T.R., Choi, C., Monroe, M.E., Thomas, S., Nie, S., Wu, C.C., Moore, R.J., Yu, K.H., Tabb, D.L., Fenyo, D., Bafna, V., Wang, Y., Rodriguez, H., Boja, E.S., Hiltke, T., Rivers, R.C., Sokoll, L., Zhu, H., Shih, I.M., Cope, L., Pandey, A., Zhang, B., Snyder, M.P., Levine, D.A., Smith, R.D., Chan, D.W., Rodland, K.D., Investigators, C.: Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166(3), 755–765 (2016). https://doi.org/10.1016/j.cell.2016.05.069

    Article  Google Scholar 

  24. Hou, Y., Braun, D.R., Michel, C.R., Klassen, J.L., Adnani, N., Wyche, T.P., Bugni, T.S.: Microbial strain prioritization using metabolomics tools for the discovery of natural products. Anal Chem 84(10), 4277–4283 (2012). https://doi.org/10.1021/ac202623g

    Article  Google Scholar 

  25. Tao, G.C., Lestander, T.A., Geladi, P., Xiong, S.J.: Biomass properties in association with plant species and assortments I: a synthesis based on literature data of energy properties. Renew. Sust. Energy Rev. 16(5), 3481–3506 (2012). https://doi.org/10.1016/j.rser.2012.02.039

    Article  Google Scholar 

  26. Tao, G.C., Geladi, P., Lestander, T.A., Xiong, S.J.: Biomass properties in association with plant species and assortments II: a synthesis based on literature data for ash elements. Renew. Sust. Energy Rev. 16(5), 3507–3522 (2012). https://doi.org/10.1016/j.rser.2012.01.023

    Article  Google Scholar 

  27. Niu, W.J., Han, L.J., Liu, X., Huang, G.Q., Chen, L.J., Xiao, W.H., Yang, Z.L.: Twenty-two compositional characterizations and theoretical energy potentials of extensively diversified China's crop residues. Energy 100, 238–250 (2016). https://doi.org/10.1016/j.energy.2016.01.093

    Article  Google Scholar 

  28. Culman, S.W., Gauch, H.G., Blackwood, C.B., Thies, J.E.: Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study. J. Microbiol. Methods 75(1), 55–63 (2008). https://doi.org/10.1016/j.mimet.2008.04.011

    Article  Google Scholar 

  29. Malanson, G.P., Bengtson, L.E., Fagre, D.B.: Geomorphic determinants of species composition of Alpine Tundra, Glacier National Park, USA. Arct. Antarct. Alp. Res. 44(2), 197–209 (2012). https://doi.org/10.1657/1938-4246-44.2.197

    Article  Google Scholar 

  30. USDA, C.: TMECC 02.02 laboratory sample preparation for analysis. In: vol. 02.02. United States Composting Council, North Carolina (2002)

  31. USDA, C.: Test methods for the examination of composting and compost (TMECC): total solids and moisture at 70±5°C (03.09-A). In: vol. 03.09-A. United States Composting Council, North Carolina, USA, (2001)

  32. Kovar, J.L.: Methods of determination of P, K, Ca, Mg and trace elements. In: Recommended methods of manure analysis. pp. 39–47. Cooperative Extension Publishing Operations, Wisconsin (2003)

  33. ASTM: ASTM E872–82 standard test method for volatile matter in the analysis of particulate wood fuels. In: vol. E872–82. ASTM International (2006)

  34. ASTM: ASTM E870–82 standard test method for analysis of wood fuels. In: vol. E870–82. ASTM International (2006)

  35. USDA, C.: Test methods for the examination of composting and compost (tmecc): organic matter—loss on ignition method (05.07-A). In: vol. 05.07-A. United States Composting Council, North Carolina (2001)

  36. AOAC: AOAC official method 993.13 nitrogen (total) in fertilizers. In: vol. AOAC 993.13. AOAC International (2000)

  37. ASTM: ASTM E777–08 standard test method for carbon and hydrogen in the analysis sample of refuse-derived fuel. In: vol. E777–08. ASTM International (2008)

  38. ISO: ISO351–1996 Solid mineral fuels: determination of total sulfur: high temperature combustion method. In: vol. ISO351–1996. International Organization for Standardization, Switzerland (1996)

  39. ASTM: ASTM E711–87 standard test method for gross calorific value of refuse-derived fuel by the bomb calorimeter (2004)

  40. Gil, M.V., Calvo, L.F., Blanco, D., Sanchez, M.E.: Assessing the agronomic and environmental effects of the application of cattle manure compost on soil by multivariate methods. Bioresource Technol. 99(13), 5763–5772 (2008). https://doi.org/10.1016/j.biortech.2007.10.014

    Article  Google Scholar 

  41. Ma, Z.: Advanced Biostatistics. Science Press, Beijing (2016)

    Google Scholar 

  42. Murtagh, F., Legendre, P.: Ward's hierarchical agglomerative clustering method: which algorithms implement ward's criterion? J. Classif. 31(3), 274–295 (2014). https://doi.org/10.1007/s00357-014-9161-z

    Article  MathSciNet  MATH  Google Scholar 

  43. Beccaccia, A., Ferrer, P., Ibanez, M.A., Estelles, F., Rodriguez, C., Moset, V., de Blas, C., Calvet, S., Garcia-Rebollar, P.: Relationships among slurry characteristics and gaseous emissions at different types of commercial Spanish pig farms. Span. J. Agric. Res. (2015). https://doi.org/10.5424/sjar/2015131-6575

    Article  Google Scholar 

  44. Hill, M.O., Gauch, H.G.: Detrended correspondence-analysis—an improved ordination technique. Vegetation 42(1–3), 47–58 (1980). https://doi.org/10.1007/Bf00048870

    Article  Google Scholar 

  45. Bello, A., Korver, D.R.: Long-term effects of Buttiauxella sp. phytase on performance, eggshell quality, apparent ileal Ca and P digestibility, and bone properties of white egg layers. Poultry Sci. 98(10), 4848–4859 (2019). https://doi.org/10.3382/ps/pez220

    Article  Google Scholar 

  46. Kumari, K.N.R., Nath, D.N.: Ameliorative measures to counter heat stress in poultry. World Poultry Sci. J. 74(1), 117–129 (2018). https://doi.org/10.1017/S0043933917001003

    Article  Google Scholar 

  47. Zhou, S.M., Han, L.J., Huang, G.Q., Yang, Z.L., Peng, J.Z.: Pyrolysis characteristics and gaseous product release properties of different livestock and poultry manures: comparative study regarding influence of inherent alkali metals. J. Anal. Appl. Pyrolysis 134, 343–350 (2018). https://doi.org/10.1016/j.jaap.2018.06.024

    Article  Google Scholar 

  48. Wang, X., Yang, Z., Liu, X., Huang, G., Xiao, W., Han, L.: The composition characteristics of different crop straw types and their multivariate analysis and comparison. Waste Manage 110, 87–97 (2020). https://doi.org/10.1016/j.wasman.2020.05.018

    Article  Google Scholar 

  49. Wang, C., Wang, T.: Feed Science. Textbook Series for 21st Century. China Agriculture Press, Beijing (2003)

    Google Scholar 

  50. Wu, J.: Animal Nutrition. Anhui Science & Technology Publishing House, Anhui (2010)

    Google Scholar 

  51. Yuan, Z., Wu, C., Ma, L.: Biomass Energy Utilization Principles and Techniques. Renewable Energy. Chemical Industry Press, Beijing (2005)

    Google Scholar 

  52. McKendry, P.: Energy production from biomass (part 1): overview of biomass. Bioresource Technol. 83(1), 37–46 (2002). https://doi.org/10.1016/s0960-8524(01)00118-3

    Article  Google Scholar 

  53. Demirbas, A.: Relationships between heating value and lignin, fixed carbon, and volatile material contents of shells from biomass products. Energy Sources 25(7), 629–635 (2003). https://doi.org/10.1080/00908310390212336

    Article  Google Scholar 

  54. Li, C.: Advanced Plant Nutrition, 2nd edn. China Agricultural University Press, Beijing (2015)

    Google Scholar 

  55. Zhang, F.: Soil Testing and Fertilization Recommendation. China Agricultural University Press, Beijing (2011)

    Google Scholar 

Download references

Acknowledgements

This research was supported by the China Agriculture Research System (CARS-36), the National Key R&D Program of China (No. 2016YFE0112800) and the Special Fund for Agro-Scientific Research Projects in the Public Interest (No. 201003063).

Author information

Authors and Affiliations

  1. Engineering Laboratory for Agro-Biomass Recycling & Valorizing, College of Engineering, China Agricultural University, Beijing, 100083, China

    Xinlei Wang, Zengling Yang, Xian Liu, Guangqun Huang, Weihua Xiao & Lujia Han

Authors
  1. Xinlei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zengling Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangqun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weihua Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lujia Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding author

Correspondence to Lujia Han.

Ethics declarations

Conflict of interest

There is no conflict of interest from authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Yang, Z., Liu, X. et al. Characteristics and Non-parametric Multivariate Data Mining Analysis and Comparison of Extensively Diversified Animal Manure. Waste Biomass Valor 12, 2343–2355 (2021). https://doi.org/10.1007/s12649-020-01178-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12649-020-01178-z

Keywords

Search

Navigation