Land capability evaluation using NRCS agricultural land evaluation and site assessment (LESA) system in a semi-arid region of Iran

Abstract

To achieve the sustainable agriculture, land capability evaluation in an accurate manner is essential. It also plays an important role to determine either the main potentials or associated limitations of the land. This research aimed to apply the NRCS agricultural land evaluation and site assessment (LESA) system integrated with GIS for classifying and mapping the capability of some calcareous soils in central of Iran as a semi-arid region for agricultural production purposes. The aforementioned model includes two sections: (1) LE and (2) SA. The first section contains soil productivity index and prime farmland, while the second one includes the effect of non-soil characteristics, development pressures, and public values on farming practices. The results showed that the specific weights for LE and SA sections were calculated 0.4 and 0.6, respectively. Integrating LESA outputs with GIS revealed that the 5.34%, 20.85%, 38.38%, and 35.42% of the studied area were, respectively, classified as “best”, “good”, “marginal”, and “not-suitable” area for agriculture productions. The marginal and not-suitable lands can be considered for developing the rangelands, agroforestry, as well as shrubs. It was concluded that the created thematic maps assist decision-makers for land-use planning and risk-based management for agriculture productions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Akinci H, Ozalp AY, Turgut B (2013) Agriculture land-use suitability analysis using GIS and AHP technique. Comput Electron Agric 97:71–82

    Article  Google Scholar 

  2. Bagheri Bodaghabadi M (2011) Applied land evaluation and land-use planning, 2nd edn. Pelk Publication, Tehran, p 385

    Google Scholar 

  3. Boyer JS, James RA, Munns R, Condon TAG, Passioura JB (2008) Osmotic adjustment leads to anomalously low estimates of relative water content in wheat and barley. Funct Plant Biol 35(11):1172–1182

    Article  Google Scholar 

  4. Bozdag A, Yavuz F, Gunay AS (2016) AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environ Earth Sci 75:813

    Article  Google Scholar 

  5. Brady NC, Weil RR (2002) The nature and properties of soils, 13th edn. Prentice Hall, New Jersey

    Google Scholar 

  6. Cambardella CA, Moorman TB, Nocak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58:1501–1511

    Article  Google Scholar 

  7. De La Rosa D, Mayol F, Diaz-Pereira E, Fernandez M, De La Rosa JD (2004) A land evaluation decision support system (MicroLEIS DSS) for agricultural soil protection with special reference to the Mediterranean region. Environ Model Softw 19:929–942

    Article  Google Scholar 

  8. Dung EJ, Sugumaran R (2005) Development of an agricultural land evaluation and site assessment (LESA) decision support tool using remote sensing and geographic information system. J Soil Water Conserv 60:228–235

    Google Scholar 

  9. Eishoeei E, Nazarnejad H, Miryaghoubzadeh M (2019) Temporal soil salinity modeling using SaltMod model in the west side of Urmia hyper saline Lake. Iran Catena 176:306–314

    Article  Google Scholar 

  10. Eldeiry AA, Garcia LA (2010) Comparison of regression kriging and cokriging techniques to estimate soil salinity using Landsat images. J Irrig Drain Eng 136:355–364

    Article  Google Scholar 

  11. FAO (1976) A framework for land evaluation. Food and Agriculture Organization of the United Nations, Soils Bulletin No.32. FAO, Rome

  12. Gee GW, Bauder JW (1986) Particle size analysis. In: Klute A (ed) Methods of soil analysis: part 1 agronomy handbook no 9. American Society of Agronomy and Soil Science Society of America, Madison, pp 383–411

    Google Scholar 

  13. Ghorbani-Dashtaki S, Homaee M, Mahdian MH, Kouchakzadeh M (2009) Site dependence performance of infiltration models. Water Resour Manag 23:1573–1650

    Article  Google Scholar 

  14. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York

    Google Scholar 

  15. LESA Handbook (2011) National agricultural land evaluation and site assessment (LESA) handbook. The Natural Resources Conservation Service (NRCS). U.S. Department of Agriculture, Washington, DC

  16. Hoobler BM, Vance GF, Hamerlinck JD, Munn LC, Hayward JA (2003) Applications of land evaluation and site assessment (LESA) and a geographic information system (GIS) in East Park County, Wyoming. J Soil Water Conserv 58:105–112

    Google Scholar 

  17. Kazemi H, Sadeghi S, Akinci H (2016) Developing a land evaluation model for faba bean cultivation using geographic information system and multi-criteria analysis (a case study: Gonbad-Kavous region, Iran). Ecol Ind 63:37–47

    Article  Google Scholar 

  18. Lavkulich LM (1981) Methods manual: pedology laboratory. University of British Columbia, Vancouver, Department of Soil Science

  19. Li Y (2010) Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information? Geoderma 159:63–75

    Article  Google Scholar 

  20. MAFF (Ministry of Agriculture, Fisheries and Food) (1988) Agricultural land classification of England and Wales. Ministry of Agriculture, Fisheries and Food, UK

  21. Mahler PJ (1979) Manual of land classification for irrigation. Soil Inst Iran 205:12–54

    Google Scholar 

  22. Mathews LG, Rex A (2011) Incorporating scenic quality and cultural heritage into farmland valuation: results from an enhanced LESA model. J Conserv Plann 7:39–59

    Google Scholar 

  23. Mirzaee S, Ghorbani-Dashtaki S, Mohammadi J, Asadi H, Asadzadeh F (2016) Spatial variability of soil organic matter using remote sensing data. CATENA 145:118–127

    Article  Google Scholar 

  24. Mirzaee S, Ghorbani-Dashtaki S, Kerry R (2020) Comparison of a spatial, spatial and hybrid methods for predicting inter-rill and rill soil sensitivity to erosion at the field scale. CATENA 188:104439

    Article  Google Scholar 

  25. Munns R, Tester M (2008) Mechanisms of salinity tolerance. Annu Rev Plant Biol 59:651–681

    Article  Google Scholar 

  26. Nelson RE (1982) Carbonate and gypsum. In: Page AL, Miller RH, Keeney DR (eds) Methods of soil analysis, part 2. Agronomy monographs, vol 9. ASA, Madison, pp 181–197

    Google Scholar 

  27. Nelson DW, Sommers LP (1986) Total carbon, organic carbon and organic matter. In: Page AL (ed) Methods of soil analysis: part 2. Agronomy handbook no 9. America Society of Agronomy and Soil Science Society of America, Madison, pp 539–579

    Google Scholar 

  28. Ostovari Y, Ghorbani-Dashtak S, Bahrami HA, Naderi M, Dematte JAM, Kerry R (2016) Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran. Geomorphology 273:385–395

    Article  Google Scholar 

  29. Ostovari Y, Honarbakhsh A, Sangoony H, Zolfaghari F, Malekie K, Ingram B (2019) GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions. Ecol Indic 103:479–487

    Article  Google Scholar 

  30. Ostovari Y, Moosavi AA, Pourghasemi HR (2020) Soil loss tolerance in calcareous soils of a semi-arid region: evaluation, prediction, and influential parameters. Land Degrad Dev 31(15):2156–2167

  31. Page AL, Miller RH, Keeney DR (1982) Methods of soil analysis (part 2): chemical and microbiological properties, 2nd edn. Society of Agronomy, Madison

    Google Scholar 

  32. Piccini C, Marchetti A, Francaviglia R (2014) Estimation of soil organic matter by geostatistical methods: use of auxiliary information in agriculture and environmental assessment. Ecol Indic 36:301–314

    Article  Google Scholar 

  33. Pilevar AR, Matinfar HR, Sohrabi A, Sarmadian F (2020) Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming. Ecol Ind 110:105887

    Article  Google Scholar 

  34. Rossiter DG, Van Wambeke AR (1994) Ales: automated land evaluation system. Version 4.1. Department of soil, crop and atmospheric sciences, Cornell University, Ithaca

  35. Shahbazi F, Jafarzadeh A, Shahbazi M (2009) Agro-ecological field vulnerability evaluation and climate change impacts in Souma area (Iran), using MicroLEIS DSS. Biologia 64(3):555–559

    Article  Google Scholar 

  36. Triantafilis J, Odeh IOA, McBratney AB (2001) Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Sci Soc Am J 65:869–878

    Article  Google Scholar 

  37. Webster R, Oliver MA (2001) Geostatistics for environmental scientist. Wiley, New York

    Google Scholar 

  38. Wu C, Wu J, Luo Y, Zhang L, DeGloria SD (2009) Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Sci Soc Am J 73:1202–1208

    Article  Google Scholar 

  39. Zekai S (2009) Spatial modeling principles in earth sciences. Springer, Berlin

    Google Scholar 

  40. Zhang J, Su Y, Wu J, Liang H (2015) GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Comput Electron Agric 114:202–211

    Article  Google Scholar 

  41. Zhu Q, Lin HS (2010) Comparing ordinary kriging and regression kriging for soil properties in the contrasting landscape. Pedosphere 20:594–606

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ensieh Esmaeili.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Verify currency and authenticity via CrossMark

Cite this article

Esmaeili, E., Shahbazi, F., Sarmadian, F. et al. Land capability evaluation using NRCS agricultural land evaluation and site assessment (LESA) system in a semi-arid region of Iran. Environ Earth Sci 80, 163 (2021). https://doi.org/10.1007/s12665-021-09468-y

Download citation

Keywords

  • Land capability
  • LESA system
  • Semi-arid
  • Thematic maps
  • GIS