In Press (Winter)                   Back to the articles list | Back to browse issues page


XML Persian Abstract Print


Department of Soil Science, Faculty of Agriculture, University of Tabriz, Tabriz , hossenrezaei@tabrizu.ac.ir
Abstract:   (510 Views)
1- Introduction
The lands sustainability depends on soil stability as one of its components. Aggregate stability is an index for evaluating soil quality and the main factor of soil stability. Soil organic matter and clay are known as the most important soil properties that play a main role in the stability of soil aggregates. Therefore, in recent studies, the ratio of soil organic carbon to clay has been expressed as an indicator of soil aggregate stability. In a large scale, land management based on soil properties such as soil aggregate stability, requires the preparation of soil aggregate stability index maps. In this regard, Digital Soil Mapping can be a useful tool. Generating soil characteristics maps from combining digital maps of soil routine properties, can be a suitable solution to facilitate soil survey studies. The present research work was designed to investigate the possibility of using secondary maps produced from the combination of soil properties maps that focus on the condition of land sustainability based on soil aggregate stability.
2- Material and methods
The present research work, which was conducted in northwestern Iran, was completed in line with a previous study in which the preparation of a digital map of clay and soil organic carbon was one of its results. The soils of study area are Inceptisols and Aridisols which are under agriculture land use. According to the main aim of the study, aggregate stability was determined by two methods for 15 study points at five standardized depth intervals (H1: 0-5, H2: 5-15, H3: 15-30, H4: 30-60, and H5: 60-100 cm) based on the GlobalSoilMap protocol. Firstly, the aggregate stability index, the ratio of soil organic carbon to clay content, was extracted from the map generated by combining a digital map of clay and soil organic map according to the formula of mentioned index. Also, aggregate stability was determined in the laboratory by wet aggregate stability method for study points. Finally complete randomized factorial designed at the same five standard depths as the statistical method to assess the performance of the generated map based on soil aggregate stability index.
3- Results
It was found that the clay and organic carbon content were ranged from 12.02 to 65.25 and 0.18 to 3.42 all over the study area. Also, descriptive statistics of aggregate stability by laboratory analysis revealed the min and max values were 8.34, and, 47.17 respectively. The results showed that with depth increment, soil organic carbon decreased while clay and aggregate stability increased. The digital maps of soil organic carbon and clay which produced in previous study showed that there aren’t distinct trends around different direct of study area. The result of generated map from combining based on aggregate stability index, the ratio of soil organic carbon to clay content, showed that with depth increment, aggregate stability increased. The Kolmogorov–Smirnov (K-S) test, showed that the obtained data for aggregate stability from studied methods do not have a normal distribution. Therefore before statistical analysis, data were normalized by Sin conversion method. The statistical analyses showed that there was no significant difference between directly measured aggregate stability index in laboratory and the data extracted from generated maps.
4- Discussion & Conclusions
From the alignment of changes in the aggregate stability index with the clay content and its contradiction with changes in the organic carbon content, it can be concluded that although organic carbon and clay are known to be the main components in aggregates stability, but their ratio ​​can also play an important role in soil aggregates stability. Also, it should be paid to the role of other factors involved in the aggregates stability, such as the diagnostic horizons that contain various salts and particles. According to statistical analysis, although there are differences between aggregate stability index from studied methods, but it is not significant. The final digital map prepared by integrating soil organic carbon and clay demonstrates an acceptable result regarding soil aggregate stability at the studied standardized depths. In this regard, it seems that using of indexes with powerful algorithm and larger number of soil characteristics lead to have higher accuracy and precision in estimating aggregate stability. Finally, it can be concluded that the usage of popular indices with a focus on the importance of contributed environmental covariates for mapping base characteristics responds to soil surveying demands. The present research revealed the crucial role of base maps for preparing key indices to improve the efficacy of soil resource management observations.
 
Full-Text [PDF 1964 kb]   (17 Downloads)    

Received: 2025/05/25

References
1. Amezketa, E. (1999). Soil aggregate stability. A Review Journal of Sustainable Agriculture, 14(2-3), 83-151. [DOI:10.1300/J064v14n02_08]
2. Arrouays, D., McKenzie, N., Hempel, J., Richer de Forges, A.C. & McBratney, A.B.) 2014(. GlobalSoilMap: Basis of the Global Spatial Soil Information System. CRC Press, London. [DOI:10.1201/b16500]
3. Arshad, M., Li, N., Di. Bella, L. & Triantafilis, J. (2020). Field-scale digital soil mapping of clay: Combining different proximal sensed data and comparing various statistical models. Soil Science Society of America Journal, 84(2), 314-330. [DOI:10.1002/saj2.20008]
4. Banaei, M.H. (1998). Soil Moisture and Temperature Regime Map of Iran. Soil and Water Research Institute, Ministry of Agriculture, Iran. (in persian)
5. Barzgar, A.A. (2004). Soil physics principle. Second publication, Shahid Chmaran University publication.
6. Bouslihim, Y., Rochdi, A., Aboutayeb, R., El Amrani-Paaza, N., Miftah, A. & Hssaini, L. (2021). Soil aggregate stability mapping using remote sensing and GIS-based machine learning technique. Environmental Informatics and Remote Sensing, 9. [DOI:10.3389/feart.2021.748859]
7. Bronick, C.J. & Lal, R. (2005). Soil structure and management: a review. Geoderma, 124(1-2), 3-22. [DOI:10.1016/j.geoderma.2004.03.005]
8. Carré, F., McBratney, A.B., Mayr, T. & Montanarella, L. (2007). Digital soil assessments: [DOI:10.1016/j.geoderma.2007.08.015]
9. Beyond DSM. Geoderma, 142, 69-79. [DOI:10.1016/j.geoderma.2007.08.015]
10. Chen, T., Niu, R.Q., Li, P.X., Zhang, L.P. & Du, B. (2011). Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun watershed, North China. Environmental Earth Sciences, 63(3), 533-541. http://dx.doi.org/10.1007/s12665-010-0715-z [DOI:10.1007/s12665-010-0715-z]
11. Darvishzadeh, A. (1991). Geology of Iran. Amir Kabir, Tehran. (in persian)
12. GSI. (2006). Geological map of Iran 1:100000, Sheet 5263. Geological Survey and Mineral Explorations of Iran, Ministry of Industry, Mine and Trade.
13. Herrick, J.E., Whitford, W.G., De Soyza, A., Van Zee, J.W., Havstad, K.M., Seybold, C. & Walton, M. (2001). Field soil aggregate stability kit for quality and rangeland health evaluations. Catena, 44(1), 27-35. http://dx.doi.org/10.1016/S0341-8162(00)00173-9 [DOI:10.1016/S0341-8162(00)00173-9]
14. IRIMO, (2020). Islamic Republic of Iran Meteorological Organization. Tehran, Iran.
15. Kamamia, A.W., Vogel, C., Mwangi, H.M., Feger, K.H., Sang, J. & Julich, S. (2021). Mapping soil aggregate stability using digital soil mapping: A case study of Ruiru reservoir catchment, Kenya. Geoderma Regional, 24, e00355. [DOI:10.1016/j.geodrs.2020.e00355]
16. Kemper, W.D. & Rosenau, R.C. (1986). Aggregate Stability and Size Distribution. In: Klute, A., (ed.). Methods of Soil Analysis. Part 1: Physical and Mineralogical Methods, 2nd Edition, Soil Science Society of America Agronomy Monograph No. 9, 425-442. [DOI:10.2136/sssabookser5.1.2ed.c17]
17. Khanifar, J., Khademalrasoul, A. & Amerikhah, H. (2020). Modelling of soil aggregate stability as an index of soil erodibility using geomorphometric parameters. Agricultural Engineering, 43(1), 49-64. [DOI:10.22055/agen.2020.28561.1482]
18. Khosravani, P., Moosavi, A.A., Baghernejad, M., Kebonye, N., Mousavi, S.R. & Scholten, T. (2024). Machine learning enhances soil aggregate stability mapping for effective land management in a semi-arid region. Remote Sensing, 16(22). [DOI:10.3390/rs16224304]
19. Le Bissonnais, Y. (1996). Soil Characteristics and Aggregate Stability. In: Agassi, M., (ed.). Soil Erosion, Conservation, and Rehabilitation, 1nd Edition, CRC Press, 41-60. [DOI:10.1201/9781003418177-3]
20. Li, M., Han, X., Du, S. & Li, L.J. (2019). Profile stock of soil organic carbon and distribution in croplands of Northeast China. Catena, 174, 285-292. [DOI:10.1016/j.catena.2018.11.027]
21. Li, H., Chang, L., Wei, Y. & Li, Y. (2023). Interacting effects of land use type, soil attributes, and environmental factors on aggregate stability. Land, 12, 1286. [DOI:10.3390/land12071286]
22. Minasny, B. & McBratney, A.B. (2016). Digital soil mapping: a brief history and some lessons. Geoderma, 264, 301-311. http://dx.doi.org/10.1016/j.geoderma.2015.07.017 [DOI:10.1016/j.geoderma.2015.07.017]
23. Monavvar Sabegh, S., Zare Haghi, D., Samadianfard, S. & Rezaei, H. (2024). Wet aggregate stability modeling based on random forest optimized with genetic
24. algorithm. Iranian Journal of Soil and Water Research, 55(7), 1095-1111. (in persian) [DOI:10.22059/ijswr.2024.376443.669712]
25. Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. & Schmid, M. (2019). A review of spline function procedures in R. BMC Medical Research Methodology, 19(1), 46. [DOI:10.1186/s12874-019-0666-3]
26. Prout, J.M., Shepherd, K.D., McGrath, S.P., Kirk, G.J.D. & Haefele, S.M. (2021). What is a good level of soil organic matter? An index based on organic carbon to clay ratio. European Journal of Soil Science, 72(6), 2493-2503. [DOI:10.1111/ejss.13012]
27. Rahbar Alam Shirazi, F., Shahbazi, F., Rezaei, H. & Biswas, A. (2023). Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates. Soil Use and Management, 39(2), 948-974. [DOI:10.1111/sum.12900]
28. Rahbar Alam Shirazi, F., Shahbazi, F., Rezaei, H. & Biswas, A. (2024). Multi-property digital soil mapping at 30-m spatial resolution down to 1 m using extreme gradient boosting tree model and environmental covariates. Remote Sensing Applications: Society and Environment, 33, 101123. [DOI:10.1016/j.rsase.2023.101123]
29. Shabani, A., Gholamalizadeh, A. & Golshahi, S. (2017). Predicting aggregate stability using soil properties in different land use. Journal of Agricultural Engineering, 39(2), 117-131. [DOI:10.22055/agen.2017.12608]
30. Shahbazi, F., Hughes, P., McBratney, A.B., Minasny, B. & Malone, B.P. (2019). Evaluating the spatial and vertical distribution of agriculturally important nutrients-nitrogen, phosphorous and boron-in North West Iran. Catena, 173, 71-82. [DOI:10.1016/j.catena.2018.10.005]
31. SheidaiKarkaj, E., Rezaei, H., Niknahad gharmakher, H., Jafari Footami, I. & Sharifian, A. (2019). The role of exclosure in changing aggregate stability and soil structure of rangelands
32. in Golestan province. Iranian Journal of Range and Desert Research, 26(4), 904-917. (in persian) [DOI:10.22092/ijrdr.2019.120682]
33. Soinne, H., Keskinen, R., Tähtikarhu, M., Kuva, J. & Hyväluoma, J. (2023). Effects of organic carbon and clay contents on structure-related properties of arable soils with high clay content. European Journal of Soil Science, 74(5), e13424. [DOI:10.1111/ejss.13424]
34. Teimuri Bardyani, S. & Sarmadian, F. (2024). Digital mapping of soil properties (Calcium Carbonate and soil clay percentage) using landsat 8 and Prisma satellite images by the random forest algorithm. Iranian Journal of Soil and Water Research, 55 (3), 381-399. (in persian) [DOI:10.22059/ijswr.2024.363941.669558]
35. Velázquez, F.J.B., Shahabi, M., Rezaei, H., González-Peñaloza, F., Shahbazi, F. & Anaya-Romero, M. (2022). The possibility of spatial mapping of soil organic carbon content at three depths using easy-to-obtain ancillary data in a Mediterranean area. Open Research Europe, 2(110), 110. [DOI:10.12688/openreseurope.14716.1]
36. Wang, S., Liu, Z., Obalum, S.E., Liang, C., Han, K. & Han, H. (2023). Effects of subsoiling depth on soil aggregate stability and carbon storage in a clay-loam soil. Journal of Soil Science and Plant Nutrition, 23, 3302-3312. [DOI:10.1007/s42729-023-01246-y]
37. Yang, C., Yang, L., Zhang, L. & Zhou, C. (2023). Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables. Geoderma, 437. [DOI:10.1016/j.geoderma.2023.116571]
38. Ziadat, F.M. (2007). Land suitability classification using different sources of information: Soil maps and predicted soil attributes in Jordan. Geoderma, 140(1-2), 73-80. [DOI:10.1016/j.geoderma.2007.03.004]
39. Zhang, Z., Wei, C., Xie D., Gao, M. & Zeng, X. (2008). Effects of land use patterns on soil aggregate stability in Sichuan Basin, China. Particuology, 6(3), 157-166. [DOI:10.1016/j.partic.2008.03.001]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 CC BY-NC 4.0 | Environmental Erosion Research Journal

Designed & Developed by : Yektaweb