Geospatial Assessment of Soil Organic Matter Variability at Sidi Bennour District in Doukkala Plain in Morocco

Bouasria, Abdelkrim; Ibno Namr, Khalid; Rahimi, Abdelmejid; Ettachfini, El Mostafa

doi:10.12911/22998993/142935

Artykuł - szczegóły

Tytuł artykułu

Geospatial Assessment of Soil Organic Matter Variability at Sidi Bennour District in Doukkala Plain in Morocco

Autorzy

Bouasria Abdelkrim , Ibno Namr Khalid , Rahimi Abdelmejid , Ettachfini El Mostafa

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.12911/22998993/142935

Warianty tytułu

Języki publikacji

Abstrakty

Understanding the spatial variability of soil organic matter (SOM) is critical for studying and assessing soil fertility and quality. This knowledge is important for soil management, which results in high crop yields at a reduced cost of crop production and helps to protect the environment. The benefits of an accurate interpolation of SOM spatial distribution are well known at the agricultural, economic, and ecological levels. It has been conducted this study for comparing and analyze different spatial interpolation methods to evaluate the spatial distribution of SOM in Sidi Bennour District, which is a semi-arid area in the irrigated scheme of the Doukkala Plain, Morocco. For conding this study, it was collected 368 representative soil samples at a depth of 0–30 cm. A portable global positioning system was used to obtain the location coordinates of soil sampling sites. The SOM spatial distribution was performed using four interpolation methods: inverse distance weighted and local polynomial interpolation as deterministic methods, and ordinary kriging and empirical Bayesian kriging as geostatistical methods. High SOM levels were concentrated in vertisols, and low SOM levels were observed in immature soils. The average SOM value was 1.346%, with moderate to high variability (CV = 35.720%). A low SOM concentration indicates a continuous possibility of soil degradation in the future. Ordinary kriging yielded better results than the other interpolation methods (RMSE = 0.395) with a semivariogram fitted by an exponential model, followed by inverse distance weighted (RMSE = 0.397), empirical Bayesian kriging (RMSE = 0.400), and local polynomial interpolation (RMSE = 0.412). Soil genetics and the strong influence of human activity are the major sources of SOM spatial dependence, which is moderate to low. Low SOM content levels (< 1.15%) were present in the southwestern and eastern parts of the digital map. This situation calls for the implementation of specific soil recovery measures.

Słowa kluczowe

soil organic matter Doukkala Plain spatial interpolation geostatistics soil quality irrigated perimeter Morocco

Wydawca

Polskie Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology

Czasopismo

Journal of Ecological Engineering

Rocznik

2021

Tom

Vol. 22, nr 11

Strony

120--130

Opis fizyczny

Bibliogr. 48 poz., rys., tab.

Twórcy

autor

Bouasria Abdelkrim

bouasria.a@ucd.ac.ma

Department of Geology, Faculty of Sciences, Chouaib Doukkali University, BP.20, 24000, El Jadida, Morocco

https://orcid.org/0000-0002-9101-6520

autor

Ibno Namr Khalid

Department of Geology, Faculty of Sciences, Chouaib Doukkali University, BP.20, 24000, El Jadida, Morocco

autor

Rahimi Abdelmejid

Department of Geology, Faculty of Sciences, Chouaib Doukkali University, BP.20, 24000, El Jadida, Morocco

autor

Ettachfini El Mostafa

Department of Geology, Faculty of Sciences, Chouaib Doukkali University, BP.20, 24000, El Jadida, Morocco

Bibliografia

1. Aboutayeb R., El Yousfi B., El Gharras O. 2020. Impact of no-till on physicochemical properties of vertisols in chaouia region of Morocco. Eurasian J Soil Sci. 9, 119–125. https://doi.org/10.18393/ejss.663502
2. Badraoui M. 2006. Connaissance et utilisation des ressources en sol au Maroc. Rapp général ‘50 ans développement Hum Perspect, 2025, 91–117.
3. Badraoui M., Agbani M., Soudi B. 2000. Evolution de la qualité des sols sous mise en valeur intensive au Maroc. Séminaire ‘Intensification Agric Qual des sols des eaux’, Rabat, Maroco, 2–3.
4. Badraoui M., Bouaziz A., Kabbassi M. 1993. Contraintes physiques et potentialités du milieu sol dans les Doukkala. Proj Développement amélioration la Prod céréalière en irrigué, 1.
5. Bhunia G.S., Shit P.K., Maiti R. 2018. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). J Saudi Soc Agric Sci, 17, 114–126. https://doi.org/https://doi.org/10.1016/j.jssas.2016.02.001
6. Bogunovic I., Kisic I., Mesic M., et al. 2017. Reducing sampling intensity in order to investigate spatial variability of soil pH, organic matter and available phosphorus using co-kriging techniques. A case study of acid soils in Eastern Croatia. Arch Agron Soil Sci, 63, 1852–1863. https://doi.org/10.1080/03650340.2017.1311013
7. Bouasria A., Ibno Namr K., Rahimi A., Ettachfini E.M. 2020. Soil organic matter estimation by using Landsat-8 pansharpened image and machine learning. In: 4th International Conference On Intelligent Computing in Data Sciences (ICDS). Fez, Morocco 2020, 1–8.
8. Cambardella C.A., Moorman T.B., Novak J.M., et al. 1994. Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Sci Soc Am J, 58, 1501–1511. https://doi.org/https://doi.org/10.2136/sssaj1994.03615995005800050033x
9. CPCS. 1967. Classification des sols. Grognon, France.
10. Dahan R., Boughlala M., Mrabet R., et al. 2001. A review of available Knowledge on Land Degradation in Morocco.
11. Dai F., Zhou Q., Lv Z., et al. 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol Indic, 45, 184–194. https://doi.org/10.1016/j.ecolind.2014.04.003
12. Dhaliwal S.S., Naresh R.K., Mandal A., et al. 2019. Dynamics and transformations of micronutrients in agricultural soils as influenced by organic matter build-up: A review. Environ Sustain Indic, 1–2, 100007. https://doi.org/10.1016/j.indic.2019.100007
13. Đurđević B., Jug I., Jug D., et al. 2019. Spatial variability of soil organic matter content in Eastern Croatia assessed using different interpolation methods. Int Agrophysics, 33, 31–39. https://doi.org/10.31545/intagr/104372
14. Farina R., Marchetti A., Francaviglia R., et al. 2017. Modeling regional soil C stocks and CO2 emissions under Mediterranean cropping systems and soil types. Agric Ecosyst Environ, 238, 128–141. https://doi.org/10.1016/j.agee.2016.08.015
15. Geoffroy J.-L. 1978. Carte pédologique: Plaine des Doukkala.
16. Goovaerts P. 1999. Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89, 1–45. https://doi.org/10.1016/S0016–7061(98)00078–0
17. Gribov A., Krivoruchko K. 2020. Empirical Bayesian kriging implementation and usage. Sci Total Environ, 722, 137290. https://doi.org/10.1016/j.scitotenv.2020.137290
18. Ibno Namr K., Mrabet R. 2004. Influence of agricultural management on chemical quality of a clay soil of semi-arid Morocco. J African Earth Sci, 39, 485–489. https://doi.org/10.1016/j.jafrearsci.2004.07.016
19. Johnston A.E., Poulton P.R., Coleman K. 2009. Chapter 1 Soil Organic Matter: Its Importance in Sustainable Agriculture and Carbon Dioxide Fluxes. In: Sparks DLBT-A in A (ed). Academic Press, 1–57
20. Kerry R., Oliver M.A. 2007. Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood. Geoderma, 140, 383–396. https://doi.org/https://doi.org/10.1016/j.geoderma.2007.04.019
21. Krivoruchko K., Gribov A. 2019. Evaluation of empirical Bayesian kriging. Spat Stat, 32, 100368. https://doi.org/10.1016/j.spasta.2019.100368
22. Laghrour M., Moussadek R., Mrabet R., Mekkaoui M. 2018. Effet à moyen et à long terme du semis direct sur la matière organique, la stabilité structurale et la compaction des sols argileux au Maroc. Rev Marocaine des Sci Agron Vétérinaires, 7, 356–362.
23. Lehmann J., Kleber M. 2015. The contentious nature of soil organic matter. Nature, 528, 60–68. https://doi.org/10.1038/nature16069
24. Liu D., Wang Z., Zhang B., et al. 2006. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, North-east China. Agric Ecosyst Environ, 113, 73–81. https://doi.org/10.1016/j.agee.2005.09.006
25. Liu R., Chen Y., Sun C., et al. 2014. Uncertainty analysis of total phosphorus spatial-temporal variations in the Yangtze River Estuary using different interpolation methods. Mar Pollut Bull, 86, 68–75. https://doi.org/10.1016/j.marpolbul.2014.07.041
26. Long J., Liu Y., Xing S., et al. 2020. Optimal interpolation methods for farmland soil organic matter in various landforms of a complex topography. Ecol Indic, 110, 105926. https://doi.org/10.1016/j.ecolind.2019.105926
27. Mabit L., Bernard C. 2010. Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools. Earth Surf Process Landforms, 35, 278–283. https://doi.org/10.1002/esp.1907
28. Marchetti A., Piccini C., Francaviglia R., Mabit L. 2012. Spatial Distribution of Soil Organic Matter Using Geostatistics: A Key Indicator to Assess Soil Degradation Status in Central Italy. Pedosphere, 22, 230–242. https://doi.org/10.1016/S1002–0160(12)60010–1
29. Mirzaee S., Ghorbani-Dashtaki S., Mohammadi J., et al. 2016. Spatial variability of soil organic matter using remote sensing data. CATENA, 145, 118–127. https://doi.org/10.1016/j.catena.2016.05.023
30. Mrabet R., El-Brahli A., Anibat I., Bessam F. 2004. No-tillage technology: research review of impacts on soil quality and wheat production in semiarid Morocco. In: C. C-M., D.G (eds) Options Mediterraneennes. Serie A, Seminaires Mediterraneens. Zaragoza: CIHEAM, 133–138.
31. Mrabet R., Moussadek R., Fadlaoui A., van Ranst E. 2012. Conservation agriculture in dry areas of Morocco. F Crop Res, 132, 84–94. https://doi.org/10.1016/j.fcr.2011.11.017
32. Mulla D.J., McBratney A.B. 2001. Soil spatial variability. Soil Phys Companion, 343–373. https://doi.org/10.1016/0167–1987(87)90053–5
33. Naman F., Soudi B., Adlouni C., Chiang C.N. 2015. Humic balance of soils under intensive farming: The case of soils irrigated perimeter of Doukkala in Morocco, 6, 3574–3581.
34. Oliver M.A., Webster R. 2015. The Variogram and Modelling BT – Basic Steps in Geostatistics: The Variogram and Kriging. In: Oliver MA, Webster R (eds). Springer International Publishing, Cham, 15–42
35. ORMVAD. 2020. Rapport d’activité annuel de l’ORMVA des Doukkala. El Jadida, Morocco.
36. Pang S., Li T.-X., Zhang X.-F., et al. 2011. Spatial variability of cropland lead and its influencing factors: A case study in Shuangliu county, Sichuan province, China. Geoderma, 162, 223–230. https://doi.org/https://doi.org/10.1016/j.geoderma.2011.01.002
37. Robinson T.P., Metternicht G. 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Comput Electron Agric, 50, 97–108. https://doi.org/https://doi.org/10.1016/j.compag.2005.07.003
38. Sahu B., Ghosh A.K., Seema. 2021. Deterministic and geostatistical models for predicting soil organic carbon in a 60 ha farm on Inceptisol in Varanasi, India. Geoderma Reg, 26, e00413. https://doi.org/10.1016/j.geodrs.2021.e00413
39. Schaum A. 2008. Principles of local polynomial interpolation. In: 2008 37th IEEE Applied Imagery Pattern Recognition Workshop, 1–6.
40. Soudi B., Naman F., Chiang C.N. 2000. Problématique de gestion de la matière organique des sols : cas des périmètres irrigués du Tadla et des Doukkala. Séminaire “Intensification Agric Qual des sols des eaux”, Rabat, 2–3.
41. Tripathi R., Nayak A.K., Shahid M., et al. 2015. Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging. Arab J Geosci, 8, 10693–10703. https://doi.org/10.1007/s12517–015–2003–4
42. Van Kernebeek H.R.J., Oosting S.J., Van Ittersum M.K., et al. 2016. Saving land to feed a growing population: consequences for consumption of crop and livestock products. Int J Life Cycle Assess 21, 677–687. https://doi.org/10.1007/s11367–015–0923–6
43. Venteris E.R., Basta N.T., Bigham J.M., Rea R. 2014. Modeling Spatial Patterns in Soil Arsenic to Estimate Natural Baseline Concentrations. J Environ Qual, 43, 936–946. https://doi.org/https://doi.org/10.2134/jeq2013.11.0459
44. Walkley A., Black I.A. 1934. An examination of the degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci, 37, 29–38. https://doi.org/10.1097/00010694–193401000–00003
45. Webster R., Oliver M.A. 2008. Geostatistics for Environmental Scientists: Second Edition. John Wiley and Sons, Rothamsted Research, United Kingdom.
46. Xie Y., Chen T., Lei M., et al. 2011. Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis. Chemosphere 82, 468–476. https://doi.org/10.1016/j.chemosphere.2010.09.053
47. Yang F., Zhang G.-L., Yang J.-L., et al. 2014. Organic matter controls of soil water retention in an alpine grassland and its significance for hydrological processes. J Hydrol, 519, 3010–3027. https://doi.org/10.1016/j.jhydrol.2014.10.054
48. Zhang Z., Yu D., Shi X., et al. 2011. Effects of prediction methods for detecting the temporal evolution of soil organic carbon in the Hilly Red Soil Region, China. Environ Earth Sci, 64, 319–328. https://doi.org/10.1007/s12665–010–0849-z

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-5ac630d7-1218-4ffc-8aa8-d53d782d7f87