Comparing Two Methods of Soil Data Interpretation to Improve the Reliability of Land Suitability Evaluation

Authors
K. A. Sultan 1
F. Ziadat 2
1 Ministry of Agriculture and Agrarian Reform
2 Soil Conservation / Land Management scientist
Abstract
Sustainable management of limited land and water resources is urgently needed to meet the increasing demand for food and to protect the environment. Land suitability analysis is a prerequisite in assessing and proposing sustainable land use alternatives for an area. Soil data are usually available at different levels of detail and stored in various forms, usually soil maps and/or soil observations. Soil data interpretation methods control the reliability of land suitability evaluation results. This has a serious effect on the reliability of the suitability maps, the subsequent land use decisions, and environmental modeling. This study examines the reliability of land suitability mapping using different methods of soil data interpretation – the average of land characteristics for field observations within soil map units (point-in-polygon) and spatial interpolation using field observations only (proximity to points). The degree of agreement between the two methods depends on the type of land utilization – rainfed barley (86%), open range (85%), improved range (75%), drip irrigated vegetables (69%), and drip irrigated trees (59%). This results from the difference in the limiting land characteristic that determines the suitability of each land utilization type and the pattern of spatial variation of each land characteristic in the field. Suitability maps for adaptable (indigenous) crops (such as barley and range crops), which require minimum farming inputs, are generally more accurate because they tolerate a wider range of variability. The interpolation method was more efficient in detecting the spatial distribution and extreme values of limiting land characteristics, resulting in more accurate suitability maps. Therefore, when detailed soil maps are not available, field observations could be used to derive suitability maps using an exact interpolation method.

Keywords

1. Bello-Pineda, J., Ponce-Hernández, R. and Liceaga-Correa, M. A. 2006. Incorporating GIS and MCE for Suitability Assessment Modelling of Coral Reef Resources. Environ. Monit. Assess., 114: 225-256.
2. Bogaert, P. and D'Or, D. 2002. Estimating Soil Properties from Thematic Soil Maps, the Bayesian Maximum Entropy Approach. Soil Sci. Soc. Am. J., 66: 1492-1500.
3. Burrough, P.A. 1992. Development of Intelligent Geographical Information Systems. Int. J. Geogr. Inf. Syst., 6(1): 1–11.
4. Burrough, P. A. and McDonnell, R. A. 1998. Principles of Geographical Information Systems. Oxford University Press, Oxford, UK.PP.333.
5. Davidson, D. A. 1992. The Evaluation of Land Resources. Longman, Harlow, UK. PP.198.
6. D' Angelo, M., Enne, G., Madrau, S., Percich, L., Previtali, F., Pulina, G. and Zucca, C. 2000. Mitigating Land Degradation in Mediterranean Agro Silvo-pastoral Systems: A GIS-Based Approach. Catena, 40: 37-49.
7. Dent, D. and Young, A. 1981. Soil Survey and Land Evaluation. George Allen and Unwin, London, UK. PP..277.
8. Department of Meteorology. 1995. Meteorological Stations’ Reports. Department of Meteorology, Amman, Jordan.
9. Drohan, P. J., Ciolkosz, E. J. and Petersen, G. W. 2003. Soil Survey Mapping Unit Accuracy in Forested Field Plots in Northern Pennsylvania. Soil Sci. Soc. Am. J., 67: 208-214.
10. Elaalem, M., Comber, A. and Fisher, P. 2011. A Comparison of Fuzzy AHP and Ideal Point Methods for Evaluating Land Suitability. Trans. GIS, 15 (3): 329-346.
11. Food and Agriculture Organization of the United Nations (FAO). 1976. Framework for Land Evaluation. FAO, Rome, Italy.
12. Food and Agriculture Organization of the United Nations (FAO). 1977. Guidelines for Soil Profile Description. FAO, Rome, Italy.
13. Food and Agriculture Organization of the United Nations (FAO). 1985. Guidelines: Land Evaluation for Irrigated Agriculture. Soils Bulletin 55, FAO, Rome, Italy.
14. Food and Agriculture Organization of the United Nations FAO. 1993. Guidelines for Land Use Planning. FAO Development Series No. 1, FAO, Rome, Italy.
15. Ghaffari, A., Cook, H. F. and Lee, H. C. 2000. Integrating Climate, Soil and Crop Information: a Land Suitability Study Using GIS. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4): Problems, Prospects and Research Needs, 2–8 September 2000, Banff, Alberta, Canada.
16. Hatten, C. J. and Taimeh, A. Y. 2001. Improvement of Agricultural Productivity in Arid and Semi-arid Zones of Jordan. In "A Cooperative Project between Ministry of Agriculture and the European :union:", (Eds.): Taimeh, A. Y. and Hattar, B. I.. Main Report, University of Jordan Press, Amman, Jordan, Vol. 1, PP.75.
17. Heidari, M. D., Omid. M. and Mohammadi, A. 2012. Measuring Productive Efficiency of Horticultural Greenhouses in Iran: A Data Envelopment Analysis Approach, Expert Syst. Appl., 39(1):1040-1045.
18. Hosseini, S.S., Tahmasebi, M. and Peykani Gh.R. 2009. Economic Factors Influencing the Decision to Plant Almonds on Sloping Land in Saman, Iran. J Agric Sci Technol, 11: 9-16.
19. Keshavarzi, A., Sarmadian, F., Heidari, A. and Omid, M. 2010. Land Suitability Evaluation Using Fuzzy Continuous Classification (A Case Study: Ziaran Region). Mod. Appl. Sci., 4(7): 72-81.
20. Mahmoodi, S. H., Heydari, A., Masihabadi, M. H. and Stoops, G. 2007. Soil-landscape Relationship as Indicated by Micromorphological Data on Selected Soils from Karaj Basin, Iran. J. Agric. Sci. Technol., 9: 153-164.
21. Mashizi, S. G. N., Keshavarzi, A., Ahmadi, A. and Etesami, H. 2009. Land Suitability Evaluation for Important Crop Productions in Gypsiferous Soils of Bardsir Area in Kerman Province, Iran. World Appl. Sci.,J, 7(6): 726-730.
22. Mazahreh, S. 1998. Alternatives for Land Utilization in Arid to Semi-Arid Regions in Jordan. Unpublished Masters’ Thesis, University of Jordan, Amman, Jordan.
23. Melitz, P.J. 1986. The Sufficiency Concept in Land Evaluation. Soil Surv. Land Eval., 6(1): 9-19.
24. Mermut, A. R. and Eswaran, H. 2001. Some Major Developments in Soil Science since the Mid-1960s. Geoderma, 100: 403-426.
25. Ministry of Agriculture, Jordan (MoA). 1995. The Soils of Jordan. Report of the National Soil Map and Land Use Project, Undertaken by Ministry of Agriculture, Huntings Technical Services Ltd., and European Commission. Level One, Level Two, Level Three and JOSCIS Manual. Ministry of Agriculture, Amman, Jordan.
26. Moradi, A., Oladi, J., Fallah, A. and Fatehi, P. 2010. An Evaluation of the Capability of IRS-P6 Data for Monitoring Pollarding Forest Areas of Northern Zagros (Case Study: Kurdistan, Pollarded Forests of Baneh). J. Agric. Sci. Technol., 12: 299-308.
27. Naderi Khorasgani, M. and De Dapper, M. 2009. Evaluation of the Capability of Landsat MSS Data for Mapping Landforms in Arid Regions: a Case Study in the Centre of Iran. J. Agric. Sci. Technol., 11: 67-80.
28. Namdar-Khojasteh, D., Shorafa, M. and Omid, M. 2010 a. Using Artificial Neural Networks for Evaluation of Soil Water Content with Time Domain Reflectometry. Mod. Appl. Sci.,e, 4(10): 76-84.
29. Namdar-Khojasteh, D., Shorafa, M., Omid, M. and Fazeli-Shaghani, M. 2010b. Application of Artificial Neural Networks in Modeling Soil Solution Electrical Conductivity. Soil Sci., 175(9): 432-437.
30. Nisar Ahamed, T. R., Gopal Rao, K. and Murthy, J. S. R. 2000. GIS-based Fuzzy Membership Model for Crop-land Suitability Analysis. Agric. Sys., 63: 75-95.
31. Oberthur, T., Dobermann, A. and Neue, H. U. 1996. How Good is a Reconnaissance Soil Map for Agronomic Purposes? Soil Use Manage., 12: 33-43.
32. Omid, M. 2011. Design of an Expert System for Sorting Pistachio Nuts through Decision Trees and Fuzzy Logic Classifier. Expert Syst. with Appl., 38(4): 4339-4347.
33. Parvizi, Y., Gorji, M., Omid, M., Mahdian, M. H. and Amini, M. 2010. Determination of Soil Organic Carbon Variability of Rainfed Crop Land in Semi-arid Region (Neural Network Approach). Mod. Appl. Sci., 4(7): 25-33.
34. Payton, R.W., Barr, J. J. F., Martin, A., Sillitoe, P., Deckers, J. F., Gowing, J. W., Hatibu, N., Naseem, S. B., Tenywa, M. and Zuberi, M. I. 2003. Contrasting Approaches to Integrating Indigenous Knowledge about Soils and Scientific Soil Survey in East Africa and Bangladesh. Geoderma, 111: 355-386.
35. Riezebos, H. T. 1989. Application of Nested Analysis of Variance in Mapping Procedure for Land Evaluation. Soil Use Manage., 5: 25-30.
36. Rilwani, M. L. and Ikhuoria, I. A. 2006. Precision Farming with Geoinformatics: A New Paradigm for Agricultural Production in a Developing Country. Trans. GIS, 10(2): 177-197.
37. Rossiter, D. G. 1996. A Theoretical Framework for Land Evaluation (with Discussion). Geoderma, 72: 165-202.
38. Salehi, M. H., Eghbal, M. K. and Khademi, H. 2003. Comparison of Soil Variability in a Detailed and a Reconnaissance Soil Map in Central Iran. Geoderma, 111: 45–56.
39. Sarmadian, F. and Taghizadeh Mehrjardi, R. 2009. Modeling of Some Soil Properties Using Artificial Neural Network and Multivariate Regression in Gorgan Province, North Iran. Global J. Environ. Res., 2(1): 30-35.
40. Shahbazi, F., Jafarzadeh, A. A., Sarmadian, F., Neyshabouri, M. R., Oustan, Sh., Anaya-Romero, M. and De la Rosa, D. 2010. Climate Change Impact on Bioclimatic Deficiency, Using MicroLEIS DSS in Ahar Soils, Iran. J. Agric. Sci. Technol., 12: 191-201.
41. Shahid, S. A., Omar, S. A. S., Jamal, M.E., Shihab, A. and Abo-Rizq, H. 2004. Soil Survey for Farm Planning in Northern Kuwait. Kuwait J. Sci. Eng., 31(1): 43-57.
42. United States Department of Agriculture (USDA). 2003. Keys to Soil Taxonomy. 9th Edition, Soil Survey Division, Natural Resources Conservation Service, USDA, Washington, DC, USA, PP.332.
43. Van Diepen, C. A., Van Keulen, H., Wolf, J. and Berkhout, J. A. A. 1991. Land Evaluation: from Intuition to Quantification. In "Advances in Soil Science", (Ed.): Stewart, B. A.. Springer, New York, NY, USA, PP. 139-204.
44. Van Kuilenburg, J., De Gruijter, J. J., Marsman, B. A. and Bouma, J. 1982. Accuracy of Spatial Interpolation between Point Data on Soil Moisture Supply Capacity, Compared with Estimates from Mapping Units. Geoderma, 27: 311-325.
45. Zangeneh, M., Omid, M. and Akram, A. 2011. A Comparative Study Between Parametric and Artificial Neural Networks Approaches for Economical Assessment of Potato Production in Iran. Span. J. Agric. Res., 9(3): 661-671.
46. Ziadat, F. M. 2000. Application of GIS and Remote Sensing for Land Use Planning in the Arid Areas of Jordan. Unpublished Doctoral Dissertation, Cranfield University, Silsoe, UK.
47. 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.
Journal of Agricultural Science and Technology
Volume 14, Issue 6
November and December 2012
Pages 1425-1438