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Monitoring the Variations of Soil Salinity in a Palm Grove in Southern Algeria

Author

Listed:
  • Abderraouf Benslama

    (Laboratoire de Mathématiques et Sciences Appliquée, Université de Ghardaïa, BP 455, Ghardaïa 47000, Algeria)

  • Kamel Khanchoul

    (Laboratory of Soils and Sustainable Development, Badji Mokhtar University-Annaba, P.O.Box 12, Annaba 23000, Algeria)

  • Fouzi Benbrahim

    (École Normale Supérieure de Ouargla, BP 398, Haї Ennasr, Ouargla 30000, Algeria)

  • Sana Boubehziz

    (Laboratory of Soils and Sustainable Development, Badji Mokhtar University-Annaba, P.O.Box 12, Annaba 23000, Algeria)

  • Faredj Chikhi

    (Laboratoire de Mathématiques et Sciences Appliquée, Université de Ghardaïa, BP 455, Ghardaïa 47000, Algeria)

  • Jose Navarro-Pedreño

    (Department of Agrochemistry and Environment, University Miguel Hernández of Elche, 03202 Elche, Alicante, Spain)

Abstract

Soil salinity is considered the most serious socio-economic and environmental problem in arid and semi-arid regions. This study was done to estimate the soil salinity and monitor the changes in an irrigated palm grove (42 ha) that produces dates of a high quality. Topsoil samples (45 points), were taken during two different periods (May and November), the electrical conductivity (EC) and Sodium Adsorption Ratio (SAR) were determined to assess the salinity of the soil. The results of the soil analysis were interpolated using two geostatistical methods: inverse distance weighting (IDW) and ordinary Kriging (OK). The efficiency and best model of these two methods was evaluated by calculating the mean error (ME) and root mean square error (RMSE), showing that the ME of both interpolation methods was satisfactory for EC (−0.003, 0.145) and for SAR (−0.03, −0.18), but the RMSE value was lower using the IDW with both data and periods. This can explain the accuracy of the IDW interpolation method. This model showed a dominance of soil salinity distribution in the South and South-East of the study area during the first season, and for the second season, the salts were concentrated in the middle of the area. Several factors could interact in this variation such as the topographic direction of the water flow and the aridity of the climate (evaporation). From this study emerges the need to maintain a better management of agricultural water and soils, avoiding salt accumulation, to ensure a good yield and the sustainability of agriculture in arid environments.

Suggested Citation

  • Abderraouf Benslama & Kamel Khanchoul & Fouzi Benbrahim & Sana Boubehziz & Faredj Chikhi & Jose Navarro-Pedreño, 2020. "Monitoring the Variations of Soil Salinity in a Palm Grove in Southern Algeria," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6117-:d:391730
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    References listed on IDEAS

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    1. Yi Yang & David Tilman & Clarence Lehman & Jared J. Trost, 2018. "Sustainable intensification of high-diversity biomass production for optimal biofuel benefits," Nature Sustainability, Nature, vol. 1(11), pages 686-692, November.
    2. Li Xu & Hongru Du & Xiaolei Zhang, 2019. "Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
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    Cited by:

    1. Li Lu & Sheng Li & Rong Wu & Deyou Shen, 2022. "Study on the Scale Effect of Spatial Variation in Soil Salinity Based on Geostatistics: A Case Study of Yingdaya River Irrigation Area," Land, MDPI, vol. 11(10), pages 1-19, September.
    2. Zengming Ke & Xiaoli Liu & Lihui Ma & Feng Jiao & Zhanli Wang, 2023. "Spatial Distribution of Soil Water and Salt in a Slightly Salinized Farmland," Sustainability, MDPI, vol. 15(8), pages 1-15, April.

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