Effect of the physical fractions of organic matter on soil aggregate stabilities in three various land uses of forest, range, and agricultural lands

Document Type : Complete scientific research article

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Abstract

Background and objectives: Soil structure stability refers to the ability of a soil to hold up the solid particle arrangement and the spaces among them when face to different stresses. According to the important role of organic matters in formation and stability of soil aggregates; it appears that the different fractions of them may also have significant effects on soil aggregate stability. Furthermore, separating the different fractions of organic matter helps the identification of the sensitive and sustainable parts and their locations in aggregate structure. The purpose of this study is to evaluate the effect of different fractions of organic matters on the soil macro (>0.25 mm) and micro ( Materials and methods: Fifteen soil samples were taken from the surface (0-10 cm) using a supervised random method in three different land uses including forest, range, and agricultural lands (totally, 45 points) in Rabor region, Kerman province. After air-drying the samples and passing them through a 4 mm sieve, mean weight diameter (MWD) of soil aggregates was measured using the wet sieve method for the macro and micro aggregates. Then, different fractionations of organic matters in the macro and micro aggregates were determined using the density method. Finally, the amount of organic carbon in different fractions of organic materials and also the total amount of soil organic carbon were identified using the Walkley-Black method and then the percentage of total organic matter existed in each fraction was calculated by considering the weight and percentage of organic matter in each fraction,. Afterward, the organic matter data were used as input to the artificial neural network model. Besides, the regression relationships among the variables and soil aggregate stability were investigated.
Results: The results showed that the free light fraction of organic matter (F1) in the macro-aggregates was greater than in the micro-aggregates. Also, the F1-amount in the forest was greater than the other two investigated land uses due to the higher organic matter content. The amounts of occluded light fraction of organic matter (F2) in the agricultural lands were lower than other land uses which might be due to tillage operation, soil aggregate destruction, and release of the trapped organic matter inside of them. The parts of organic matter which were associated with mineral fractions (F3) were allocated with the largest percentage of the total soil organic matter as compared to the other two fractions. The amount of last mentioned fraction was higher in the micro-aggregates than the macro-aggregates in all three investigated land uses. Results also revealed that the linear regression was not able to identify the interrelationship between the studied variables and aggregates stability index. In contrast, the diagrams related to artificial neural network model showed that all input variables to the model have influenced the MWD, although the important coefficients of the input variables were different.
Conclusion: Different fractions of soil organic matter were more sensitive to the land use type than the total organic matter. The macro and micro aggregate percentages were different depending to the land use type; however, the micro aggregates with greater organic matter contents were more stable against entered stresses than the macro aggregates. Artificial neural network model had more efficiency in estimating the soil aggregate stability than the linear regression method indicating that there is a non-linear relationship between different fractions of organic matter and MWD. By considering the accuracy and efficiency of artificial network model, it appears that this method can be used to determine the linear and non-linear relationship among different soil properties, with higher precision and lower cost and time.

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References

1.Alijanpour Shalmani, A., Shabanpour Shahrestani, M., Asadi, H., and Bagheri, F. 2010.
Comparison of regression pedotransfer functions and artificial neural networks for soil
aggregate stability simulation. World Appl. Sci. J. 8: 9. 1065-1072.
2.An, S., Mentler, A., Mayer, H., and Blum, W.E.H. 2010. Soil aggregation, aggregate stability,
organic carbon and nitrogen in different soil aggregate fractions under forest and shrub
vegetation on the Loess Plateau, China. Catena. 81: 226-233.
3.Andesodun, J.K., Mbagwu, J.S.C., and Oti, N. 2005. Distribution of carbon, nitrogen and
phosphorus in water-stable aggregates of an organic waste amended Ultisol in southern
Nigeria. Bio-Resources Technology. 96: 509-516.
4.Bast, A., Wilcke, W., Graf, F., Luscher, P., and Gartner, H. 2015. A simplified and
rapid technique to determine an aggregate stability coefficient in coarse grained soils.
Catena. 127: 170-176.
5.Bayati, H., Najafi, A., and Abdolmaleki, P. 2013. Comparison between artificial neural
network (ANN) and regression analysis in tree felling time estimation. Iran. J. For. Pop. Res.
20: 4. 595-607. (In Persian)
6.Besalatpour, A.A., Shirani, H., and Esfandiarpour, I. 2014. Modeling of soil aggregate
stability using support vector machines and multiple linear regression. Soil Water Sci. J.
29: 2. 416-427. (In Persian)
7.Besalatpour, A.A., Ayoubi, S., Hajabbasi, M.A., Mosaddeghi, M.R., and Schulin, R. 2013.
Estimating wet soil aggregate stability from easily available properties in a highly
mountainous watershed. Catena. 111: 72-79.
8.Chaplot, V., and Cooper, M. 2015. Soil aggregate stability to predict organic carbon outputs
from soils. Geoderma. 243-244: 205-213.
9.Cheng, M., Xiang, Y., Xue, Zh., An, Sh., and Darboux, F. 2015. Soil aggregation and
intra-aggregate carbon fractions in relation to vegetation succession on the Loess Plateau,
China. Catena. 124: 77-84.
10.Emadodin, I., Reiss, S., and Rudolf Bork, H. 2009. A study of the relationship between land
management and soil aggregate stability (Case study near Albersdorf, northern-Germany).
ARPN J. Agric. Biol. Sci. 4: 48-53.
11.Grandy, A.S., and Robertson, G.P. 2006. Aggregation and organic matter protection
following tillage of a previously uncultivated soil. Soil Sci. Soc. Amer. J. 70: 1398-1406.
12.Hajabbasi, M.A., Basalatpour, A.A., and Maleki, A.R. 2007. Effect of shifting rangeland to
farmland on some physical and chemical properties of south and southwest soils of Isfahan.
Iran. J. Sci. Technol. Agric. Natur. Resour. 11: 42. 525-534. (In Persian)
13.Haynes, R.J. 2005. Labile organic matter fractions as central components of the quality of
agricultural soils: an overview. Advances in Agronomy. 85: 221-268.
14.Hinsinger, P., Bengough, A.G., Vetterlein, D., and Young, I.M. 2009. Rhizosphere:
biophysics, biogeochemistry and ecological relevance. Plant and Soil. 321: 117-152.
15.Hosseini, F., Mosaddeghi, M.R., Hajabbasi, M.A., and Sabzalian, M.R. 2015. Influence of
tall fescue endophyte infection on structural stability as quantified by high energy moisture
characteristic in a range of soils. Geoderma. 249-250: 87-99.
16.Huang, L., Wang, C.Y., Tan, W.F., Hu, H.Q., Cai, C.F., and Wang, M.K. 2010. Distribution
of organic matter in aggregates of eroded Ultisols, Central China. Soil and Tillage Research.
108: 59-67.
17.Jaksik, O., Kodesova, R., Kubis, A., Stehlikova, I., Drabek, O., and Kapicka, A. 2015. Soil
aggregate stability within morphologically diverse areas. Catena. 127: 287-299.
18.Kemper, W.D., and Rosenau, K. 1986. Size distribution of aggregates. P 425-442, In: A.
Klute (Ed.), Methods of Soil Analysis: Part 1: Physical and Mineralogical Methods,
American Society of Agronomy, Madison, WI.
19.Li, Q.Q., Yue, T.X., Wang, C.Q., Zhang, W.J., Yu, Y., Li, B., Yang, J., and Bai, G.C. 2013.
Spatially distributed modeling of soil organic matter across China: An application of
artificial neural network approach. Catena. 104: 210-218.
20.Liu, M.Y., Chang, Q.R., Qi, Y.B., Liu, J., and Chen, T. 2014. Aggregation and soil organic
carbon fractions under different land uses on the tableland of the Loess Plateau of China.
Catena. 115: 19-28.
21.Lobe, I., Hofmann, A.S., Brodowski, S., du Preez, C.C., and Amelung, W. 2011. Aggregate
dynamics and associated soil organic matter contents as influenced by prolonged arable
cropping in the South African Highveld. Geoderma. 162: 251-259.
22.Lopez-Sangil, L., and Rovira, P. 2013. Sequential chemical extractions of the mineralassociated
soil organic matter: An integrated approach for the fractionation of organomineral
complexes. Soil Biology and Biochemistry. 62: 57-67.
23.Maly, S., Fiala, P., Reininger, D., and Obdrzalkova, E. 2014. The relationships among
microbial parameters and the rate of organicmatter mineralization in forest soils, as
influenced by forest type. Pedobiologia. 57: 235-244.
24.Marzaioli, F., Lubritto, C., Galdo, I.D., Onofrio, A., Cotrufo, M.F., and Terrasi, F. 2010.
Comparison of different soil organic matter fractionation methodologies: Evidences from
ultrasensitive 14C easurements. Beam Interactions with Materials and Atoms. 268: 1062-1066.
25.McFarlane, K.J., Torn, M.S., Hanson, P.J., Porras, R.C., Swanston, C.W., Callaham Jr, M.A.,
and Guilderson, T.P. 2012. Comparison of soil organic matter dynamics at five temperate
deciduous forests with physical fractionation and radiocarbon measurements. Biogeochem.
Inter. J. 12: 1. 457-476.
26.Menhaj, M. 2001. Acquaintanceship with artificial neural networks. Sharif Univ. Press,
Tehran, Iran, 137p. (In Persian)
27.Moghimi, S., Mahdian, M.H., Parvizi, Y., and Masihabadi, M.H. 2014. Estimating effects of
terrain attributes on local soil organic carbon content in a semi-arid pastureland. J. Biodiv.
Environ. Sci. 5: 2. 97-106.
28.Mohammadi, J. 2002. Testing an artificial neural network for predicting soil water retention
characteristics from soil physical and chemical properties. 17th WCSS, Thailand, Pp: 378-943.
29.Mujuru, L., Mureva, A., Velthorst, E.J., and Hoosbeek, M.R. 2013. Land use and
management effects on soil organic matter fractions in Rhodic Ferralsols and Haplic
Arenosols in Bindura and Shamva districts of Zimbabwe. Geoderma. 209-210: 262-272.
30.Nelson, D.W., and Sommers, L.E. 1996. Total carbon, organic carbon and organic matter:
laboratory methods. P 961-1010, In: D.L. Sparks (Ed.), Methods of Soil Analysis. Part 3.
Soil Sci. Soc. Amer. J. Madison, Wisconsin.
31.Pinheiro, E.F.M., de Campos, D.V.B., Balieiro, F.C., dos Anjos, L.H.C., and Pereira, M.G.
2015. Tillage systems effects on soil carbon stock and physical fractions of soil organic
matter. Agricultural Systems. 132: 35-39.
32.Plaza, C., Courtier-Murias, D., Fernandez, J.M., Polo, A., and Simpson, A.J. 2013. Physical,
chemical and biochemical mechanisms of soil organic matter stabilization under
conservation tillage systems: A central role for microbes and microbial by-products in C
sequestration. Soil Biology and Biochemistry. 57: 124-134.
33.Robinson, T.P., and Metternicht, G. 2006. Testing the performance of spatial interpolation
techniques for mapping soil properties. Computer and Electronics in Agriculture. 50: 2. 97-108.
34.Roscoe, R., Buurman, P., Van lagen, B., and Velthorst, E. 2004. Transformations in
occluded light fraction organic matter in a clayey Oxisol; evidence from 13c-cpmas-nmr and
13C signature. Ociedade Brasileira de Ciencia do Solo. 28: 811-818.
35.Safari Sinegani, A.A. 2015. Soil organic matter. Bu-Ali Sina University Press, 364p.
36.Sedaee-azar, Z., Raufee, M., and Asghari, Sh. 2013. The effect of organic matter on soil
infiltration of different land uses. National Congress of Organic Agriculture, Pp: 1-6.
(In Persian)
37.Shirani, H., Habibi, M., Besalatpour, A.A., and Esfandiarpour, I. 2015. Determining the
features influencing physical quality of calcareous soils in a semiarid region of Iran using a
hybrid PSO-DT algorithm. Geoderma. 259-260: 1-11.
38.Six, J., Paputian, K., Elliot, E.T., and Combrink, C. 2001. Soil structure and organic matter.
I. Distribution of aggregate-size classes and aggregate-associated carbon. Soil Sci. Soc.
Amer. J. 64: 681-689.
39.Six, J., Bossuyt, H., Degryze, S., and Denef, K. 2004. A history of research on the link
between (micro) aggregates, soil biota and soil organic matter dynamics. Soil and Tillage
Research. 79: 1. 7-31.
40.Spaccini, R., and Piccolo, A. 2013. Effects of field managements for soil organic matter
stabilization on water-stable aggregate distribution and aggregate stability in three
agricultural soils. J. Geochem. Exp. 129: 45-51.
41.Wang, J.G., Yang, W., Yu, B., Li, Z.X., Cai, C.F., and Ma, R.M. 2016. Estimating the
influence of related soil properties on macro- and micro-aggregate stability in ultisols of
south-central China. Catena. 137: 545-553.
42.Wei, J.B., Xiao, D.N., Zeng, H., and Fu, Y.K. 2008. Spatial variability of soil properties in
relation to land use and topography in a typical small watershed of to black soil region,
northeastern China. Environmental Geology. 53: 1663-1672.
43.Yague, M.R., Bosch-Serra, A.D., Antunez, M., and Boixadera, J. 2012. Pig slurry and
mineral fertilization strategies' effects on soil quality: Macroaggregate stability and organic
matter fractions. Science of the Total Environment. 438: 218-224.
44.Zhang, J., Song, C., and Wenyan, Y. 2007. Tillage effects on soil carbon fractions in the
Sanjiang Plain, Northeast China. Soil and Tillage Research. 93: 1. 102-108.
Journal of Soil Management and Sustainable Production
Volume 7, Issue 2
September 2017
Pages 47-65

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