Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Soil Science, College of Agriculture Sciences, Lorestan, Iran

2 Professor, Department of Soil Science, College of Agriculture Sciences, Lorestan, Iran

3 Associate Professor, Department of Soil Science, College of Agriculture Sciences, Ilam, Iran

4 Assistant Professor, Department of Soil Science, College of Agriculture Sciences, Lorestan, Iran

Abstract

Introduction: Assessing land suitability and determining its production potential to manage soil and land resources is one of the best sustainable agricultural policies. Barley is the second most cultivated crop in Iran after wheat. However, all soils in Iran are equally suitable for barley production. This study was conducted with the aim of evaluating the suitability of land for barley cultivation using a spatial model integrated with Geographical Information System (GIS).

Materials and Methods: The suitability of the land for barley cultivation is affected by various factors including (percentage of sand, percentage of silt, percentage of clay, percentage of saturated moisture, structure, percentage of surface gravel, water retention capacity, organic matter, nitrogen, phosphorus, potassium, EC, CEC, SAR, CaCO3 and pH) that were identified in the study area. In order to evaluate the suitability of land for the production of barley crops, Cumulative Quality Index (IQI) and Numerical Quality Index (NQI) were used with two series of data sets including: Total Data Set (TDS) and Minimum Data Set (MDS) and the results of this The indices were compared with two indices, storie and square root.

Results and Discussion: 17 measured parameters were used as the total data set (TDS) and 5 parameters (sand percentage, clay percentage, silt percentage, saturated moisture and pH) were used as the minimum data set (MDS). Also, the results showed that using the data set The minimum (MDS) provides a closer estimate to the storied and square-root methods compared to using the total data set (TDS), thus even considering a limited number of effective soil properties with respect to spending less time and money on quality assessment. Soil and agricultural management can provide better results.

Conclusion: By calculating various indices and comparing them with the more common storie and square root methods, it becomes possible to survey and monitor land using new techniques. This helps validate the accuracy of the index performance. With the square root and storie techniques, the value of each parameter is categorised based on sources and conducted studies, and the requirements of the barley plant. Each parameter receives a specific grade. Considering the assigned grades and comparing them with the estimated values from the ground experiments, it is possible to identify which areas of land are more or less suitable for the intended purpose. Satellite images combined with ground observation data provide valuable information for land evaluation. The results showed that most of the units in the storie model and the square root were placed in the medium suitability class (S2) for barley production. Comparing the correlation between land suitability assessment methods and measuring soil quality indices method showed that there is the highest correlation between the NQIMDS method and the square root. In general, it can be said that the soil quality index can provide better results with minimum data set and less time and cost for soil quality assessment and agricultural management. The soil maps produced for agricultural suitability analysis in this research can serve as an effective aid in decision-making processes. Subsequent research should concentrate on employing new predictive tools to enhance forecasting abilities. Most studies have used fundamental GIS techniques for resource allocation. GIS is a potent tool for spatial analysis in resource allocation. Since land resources are decreasing rapidly, land use planning should be accomplished efficiently to recognize new areas for crop production. The use of advanced simulation software assists in the reduction of redundancy within other processes while simultaneously increasing their accuracy. Consequently, researchers must concentrate on carrying out studies concerning new and developed GIS software. Unmanned aerial vehicles (UAVs) could enhance accessibility, and therefore improve the effectiveness of resource allocation (Yu et al., 2014). Modelling techniques can be employed to evaluate the practical impact of resources.

The results of this research can be useful in managerial decisions. In future studies, the use of new predictive tools should be considered. As land resources are rapidly decreasing, effective land use planning should be considered to identify new crop production areas. The use of advanced simulation software helps to eliminate the redundancy of other processes and increase accuracy (82, 93). Therefore, researchers should focus on conducting studies related to new and improved GIS software. Unmanned aerial vehicles (UAVs) may increase access to increase the effectiveness of resource allocation (103). Modeling techniques can be used to assess the practical impact of resources.

Keywords

Main Subjects

References
1. Ahmadi, K., Ebadzadeh, H. R., Hatami, F., Abd Shah, H., and Kazemian, A. 2020. Agricultural statistics of the Crop Year 2018-2019, Volume One: Crop Products. Deputy of Planning and Economics, Information and Communication Technology Center, Tehran. (In Persian)
2. Akpoti, K., Kabo-bah, A. T., and Zwart, S. J. 2019. Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis. Agricultural systems, 173: 172-208.
3. Amirinejad, A. A., Kamble, K., Aggarwal, P., Chakraborty, D., Pradhan, S. and Mittal, R. B. 2011 . Assessment and mapping of spatial variation of soil physical health in a farm. Geoderma, 160(3-4), 292-303.
4. Andrews, S. S., Mitchell, J. P., Mancinelli, R., Karlen, D. L., Hartz, T. K., Horwath, W. R., and Munk, D. S. 2002. On‐farm assessment of soil quality in California's Central Valley. Agronomy journal, 94(1): 12-23.
5. Andrews, S. S., Karlen, D. L., and Cambardella, C. A. 2004. The soil management assessment framework: a quantitative soil quality evaluation method. Soil Science Society of America Journal, 68(6): 1945-1962.
6. Aparicio, V., and Costa, J. L. 2007. Soil quality indicators under continuous cropping systems in the Argentinean Pampas. Soil and Tillage Research, 96(1-2): 155-165.
7. Asghari, S., Dizajghoorbani Aghdam, S., and Esmali, A. 2016. Investigation te Spatial Variability of some Soil Physical Quality Indices in Fandoghlou Region of Ardabil Using Geostatistics. Water and Soil, 28(6): 1271-1283. (Persian with English abstract)
8. Banaei, H. M. 1998. Soil moisture and temperature regimes map of Iran (1: 2500000). Soil and Water Research Institute. (In Persian)
9. Bremner, J. M., and Mulvaney, C. S. 1982. Nitrogen total. Methods of soil analysis: part 2 chemical and microbiological properties, 9: 595-624.
10. Brasher, B. R., Franzmeier, D. P., Valassis, V., and Davidson, S. E. 1966. Use of saran resin to coat natural soil clods for bulk-density and water-retention measurements. Soil Science, 101(2): 108.
11. Behnia, M. R. 1997. Cereal. Second Edition. University of Tehran Press, pp: 610. (In Persian)
12. Booty, W. G., Lam, D. C. L., Wong, I. W. S., and Siconolfi, P. 2001. Design and implementation of an environmental decision support system. Environmental Modelling and Software, 16(5): 453-458.
13. Chen, J. 2014. GIS-based multi-criteria analysis for land use suitability assessment in City of Regina. Environmental Systems Research, 3: 1-10.
14. Darwish, K. M., Wahba, M. M., and Awad, F. 2006. Agricultural soil suitability of Haplo-soils for some crops in newly reclaimed areas of Egypt. Journal of Applied Sciences Research, 2(12): 1235-1243.
15. De la Rosa, D., and Van Diepen, C. A. 2002. Qualitative and quantitative land evaluation, In 1.5. Land use and land cover. Encyclopedia of Life Support System (EOLSS-UNESCO).
16. FAO. 1976. A Framework for Land Evaluation. Food and Agriculture Organization of the United Nations, Soils Bulletin No.32. FAO, Rome.
17. FAO. 1985. Guidelines: Land Evaluation for Irrigated Agriculture. Soil Bulletin No.55. FAO, Rome.
18. Gee, G. W., and Bauder, J. W. 1986. Particle‐size analysis. Methods of soil analysis: Part 1 Physical and mineralogical methods, 5: 383-411.
19. Ghanbarie, E., Jafarzadeh, A. A., Shahbazi, F., and Servati, M. 2016. Comparing parametric methods (the square root and the storie) with the fuzzy set theory for land evaluation of khaje region for wheat. International Journal of Advanced Biotechnology and Research (IJBR), 7: 343-351.
20. Govaerts, B., Sayre, K. D., and Deckers, J. 2006. A minimum data set for soil quality assessment of wheat and maize cropping in the highlands of Mexico. Soil and Tillage Research, 87(2): 163-167.
21. Halder, J. C. 2013. Land suitability assessment for crop cultivation by using remote sensing and GIS. Journal of geography and Geology, 5(3): 65-74.
22. Hamzeh, S., Mokarram, M., and Alavipanah, S. K. 2014. Combination of Fuzzy and AHP methods to assess land suitability for barley: Case Study of semi arid lands in the southwest of Iran. Desert, 19(2): 173-181.
23. Hassan, I., Javed, M. A., Asif, M., Luqman, M., Ahmad, S. R., Ahmad, A., and Hussain, B. 2020. Weighted overlay based land suitability analysis of agriculture land in Azad Jammu and Kashmir using GIS and AHP. Pakistan Journal of Agricultural Sciences, 57(6).
24. Hengl T., Rossiter D.G., and Stein A. 2003. Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Geoderma, 120:75-93.
25. Hoseini, Y., and Kamrani, M. 2018. Using a fuzzy logic decision system to optimize the land suitability evaluation for a sprinkler irrigation method. Outlook on Agriculture, 47(4): 298-307.
26. IIASA, F. 2012. Global Agro-ecological Zones–Model Documentation (GAEZ v. 3.0). International Institute of Applied Systems Analysis & Food and Agricultural Organization, Laxenburg, Austria and Rome, Italy.
27. Kemper, W. D., and Rosenau, R. C. 1986. Aggregate stability and size distribution. Methods of soil analysis: Part 1 Physical and mineralogical methods, 5: 425-442.
28. Khiddir, S. M. 1986. A statistical approach in the use of parametric systems applied to the FAO framework for land evaluation (Doctoral dissertation, Ghent University).
29. Klute, A., and Dirksen, C. 1986. Hydraulic conductivity and diffusivity: Laboratory methods. Methods of soil analysis: Part 1 physical and mineralogical methods, 5: 687-734.
30. Kurukulasuriya, P., and Mendelsohn, R. O. 2008. How will climate change shift agro-ecological zones and impact African agriculture?. World Bank Policy Research Working Paper, (4717).
31. Lanyon, L. E., and Heald, W. R. 1982. Magnesium, calcium, strontium, and barium. Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, 9: 247-262.
32. Lavkulich, L. M. 1981. Methods Manual, Pedology Laboratory. Department of Soil Science, University of British Columbia, Vancouver. British Columbia, Canada.
33. Leroux, L., Castets, M., Baron, C., Escorihuela, M. J., Bégué, A., and Seen, D. L. 2019 . Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal of Agronomy, 108: 11-26.
34. Liebig, M. A., Varvel, G., and Doran, J. 2001. A simple performance‐based index for assessing multiple agroecosystem functions. Agronomy Journal, 93(2): 313-318.
35. Maleki, P., Landi, A., Sayyad, G. H., Baninemeh, J., and Zareian, G. 2010. Application of fuzzy logic to land suitability for irrigated wheat.
36. Mohammadrezaei, N., Pazira, E., Sokoti, R., and Ahmadi, A. 2014 . Land suitability evaluation for wheat cultivation by Fuzzy-AHP, Fuzzy-Simul Theory approach as compared with parametric method in the southern plain of Urmia. Bull. Environ. Pharmacol. Life Sci, 3: 112-117.
37. Mokarram, M., Rangzan, K., Moezzi, A., and Baninemeh, J. 2010. Land suitability evaluation for wheat cultivation by fuzzy theory approach as compared with parametric method. The international archives of the photogrammetry, remote sensing and spatial information sciences, 38(Part II): 140-145.
38. Mustafa, A. A., Singh, M., Sahoo, R. N., Ahmed, N., Khanna, M., Sarangi, A., and Mishra, A. K. 2011. Land suitability analysis for different crops: a multi criteria decision making approach using remote sensing and GIS. Researcher, 3(12): 61-84.
39. Nelson, D. W., and Sommers, L. E. 1983. Total carbon, organic carbon, and organic matter. Methods of soil analysis: Part 2 chemical and microbiological properties, 9: 539-579.
40. Nelson, R. E. 1982. Carbonate and gypsum. Methods of soil analysis: Part 2 Chemical and microbiological properties, 9: 181-197.
41. Nimmo, J. R., and Perkins, K. S. 2002. 2.6 Aggregate stability and size distribution. Methods of soil analysis: part 4 physical methods, 5: 317-328.
42. Olsen, S. R. 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate (No. 939). US Department of Agriculture.
43. Pieri, C. J. 2012. Fertility of soils: a future for farming in the West African Savannah (Vol. 10). Springer Science and Business Media.
44. Qi, Y., Darilek, J. L., Huang, B., Zhao, Y., Sun, W., and Gu, Z. 2009. Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma, 149(3-4): 325-334.
45. Qin, M. Z., and Zhao, J. 2000 . Strategies for sustainable use and characteristics of soil quality changes in urban-rural marginal area. ACTA GEOGRAPHICA SINICA-CHINESE EDITION-, 55(5): 545-554.
46. Rahmanipour, F., Marzaioli, R., Bahrami, H. A., Fereidouni, Z., and Bandarabadi, S. R. 2014 . Assessment of soil quality indices in agricultural lands of Qazvin Province, Iran. Ecological indicators, 40: 19-26.
47. Rezaei, S. A., Gilkes, R. J., Andrews, S. S., and Arzani, H. 2005 . Soil quality assessment in semiarid rangeland in Iran. Soil use and management, 21(4): 402-409.
48. Rhoades, J. D. 1982. Cation exchange capacity. Methods of soil analysis: Part 2 chemical and microbiological properties, 9: 149-157.
49. Richards, L. A. 1968. Diagnosis and improvement of saline and alkali soils. Agriculture handbook, 60: 210-220.
50. Santos-Francés, F., Martínez-Graña, A., Ávila-Zarza, C., Criado, M., and Sánchez, Y. 2019. Comparison of methods for evaluating soil quality of semiarid ecosystem and evaluation of the effects of physico-chemical properties and factor soil erodibility (Northern Plateau, Spain). Geoderma, 354: 113872.
51. Shahab, H., Emami, H., Haghnia, G. H., and Karimi, A. 2013. Pore size distribution as a soil physical quality index for agricultural and pasture soils in northeastern Iran. Pedosphere, 23(3): 312-320.
52. Shields, P. G., Smith, C. D., and McDonald, W. S. 1996. Agricultural land evaluation in Australia: a review.
53. Silva-Gallegos, J. J., Aguirre-Salado, C. A., Miranda-Aragón, L., Sánchez-Díaz, G., Valdez-Lazalde, J. R., Pedroza-Carneiro, J. W., and Flores-Cano, J. A. 2017. Locating potential zones for cultivating Stevia rebaudiana in Mexico: weighted linear combination approach. Sugar Tech, 19: 206-218.
54. Soil Survey Staff. 2014. Kellogg Soil Survey Laboratory Methods Manual. Soil Survey Investigations Report No. 42, Version 5.0. U.S. Department of Agriculture, Natural Resources Conservation Service, pp: 1001.
55. Storie, R. E. 1978. Storie index soil rating. University of California, Division of Agricultural Sciences Special Publication, No. 3203, Oakland, USA.
56. Sumner, M. E., and Miller, W. P. 1996. Cation exchange capacity and exchange coefficients. Methods of soil analysis: Part 3 Chemical methods, 5: 1201-1229.
57. Sys, C., Van Ranst, E., Debaveye, J. 1991. Land Evaluation, Part I. Principles in Land Evaluation and Crop Production Calculations. General administration for development cooperation, Brussels, pp: 40-80.
58. Sys, C., Van Ranst, E., Debaveye, J., and Beernaert, F. 1993. Land Evaluation. Part III: crop requirements. Agricultural Publications n° 7, GADC, Brussels, Belgium, pp:191.
59. Teka, K., and Haftu, M. 2012. Land suitability characterization for crop and fruit production in Midlands of Tigray, Ethiopia. Momona Ethiopian Journal of Science, 4(1): 64-76.
60. Van de Graaff, R.H.M. 1988. Land Evaluation. In: Gunn, R.H., Beattie, J.A., Reid, R.E., van deGraaff, R.H.M. (Eds.), Australian Soil and Land Survey Handbook: Guidelines forConducting Surveys. Inkata Press, Sydney, pp: 258-281.
61. Vasu, D., Srivastava, R., Patil, N. G., Tiwary, P., Chandran, P., and Singh, S. K. 2018. A comparative assessment of land suitability evaluation methods for agricultural land use planning at village level. Land use policy, 79: 146-163.
62. Zadeh, L. A. 1965. Fuzzy sets. Information and control, 8(3): 338-353.