A hybrid method of parametric-factor analysis in land suitability evaluation for Ferula communis
محورهای موضوعی : LandAli Bagherzadeh 1 , Alireza Anvarkhah 2 , Maryam Tatari 3 , Majid Rahimizadeh 4
1 - Department of Agriculture, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2 - Faculty of Agriculture, Shirvan Branch, Islamic Azad University, Shirvan, Iran
3 - Faculty of Agriculture, Shirvan Branch, Islamic Azad University, Shirvan, Iran.
4 - Department of Agriculture, Bojnord Branch, Islamic Azad University, Bojnourd, Iran
کلید واژه: PCA, GIS, Land evaluation, Hybrid method, Ferula,
چکیده مقاله :
Background and objective: Due to the constant decline in arable land, it is essential to identify the best areas for sustainable agriculture. Materials and Methods: In the present study, the factor analysis (FA) by principal component analysis (PCA) method as multivariate statistical was applied to evaluate the Land suitability zonation of 36700 points for Ferula plantation in North Khorasan Province, NE Iran. For this purpose, extracted 16 variables were processed, resulting in four factors that explain about 90 % of the total variance.Results and conclusion: The explained variances of these factors varied from 25.519 to 9.078 % for factors 1 and 4 after the Varimax rotation, respectively. The zonation map of land suitability revealed that 27.02% (6885/93 km2) of the surface area was moderately suitable, 67/20% (17125/36 km2) was marginally applicable and 5.78% (1474.52 km2) of the region was unsuitable for Ferula production. The moderate suitability class of S2 was mainly distributed within the middle, and northeastern parts of the province, while the southern part of the study area and some scattered parts within the northwest exhibited unsuitability for Ferula production.
Abdelkader, M., & Delali, A. (2012). Support system based on GIS and weighted sum method for drawing up of land suitability map for agriculture. Application to durum wheat cultivation in the area of Mleta (Algeria). Spanish Journal of Agricultural Research, 10(1), 34-43. https://doi.org/10.5424/sjar/2012101-293-11
Bagherzadeh, A., Gholizadeh, A., & Alizadeh Motaghi, F. (2021). The feasibility of ash and spruce forest plantations in the Northeast of Iran. Environmental Resources Research, 9(1), 1-12.
Ball, A., & De la Rosa, D. (2006). Modeling possibilities for the assessment of soil systems. In Biological approaches to sustainable soil systems (pp. 683-692). Taylor & Francis. https://doi.org/10.1201/9781420017113.ch48
Bartzokas, A., & Metaxas, D. A. (1995). Factor analysis of some climatological elements in Athens, 1931–1992: covariability and climatic change. Theoretical and applied climatology, 52, 195-205. https://doi.org/10.1007/BF00864043
Bukantis, A. (2002). Application of factor analysis for quantification of climate-forming processes in the eastern part of the Baltic Sea region. Climate Research, 20(2), 135-140. https://doi.org/10.3354/cr020135
FAO (1976) A framework for land evaluation: Soils Bulletin 32, Food and Agriculture Organization of the United Nations, Rome, Italy
FAO (1983). Guidelines: Land evaluation for rainfed agriculture. FAO Soils Bulletin. No. 52, Rome.
FAO (1984) Land evaluation for forestry, forestry paper 48: Food and Agriculture Organization of the United Nations, Rome, Italy
FAO (1985). Guidelines: Land evaluation for irrigated agriculture. FAO Soils Bulletin, No. 55, Rome.
Ghane Ezabadi, N., Azhdar, S., & Jamali, A. A. (2021). Analysis of dust changes using satellite images in Giovanni NASA and Sentinel in Google Earth Engine in western Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 17-26. https://doi.org/10.30495/jonass.2021.680327
Gorsuch, R. L. (1983). Factor Analysis, 2nd edn Lawrence Erlbaum Associates. Hillsdale, NJ.
He, S., Wang, D., Li, Y., Zhao, P., Lan, H., Chen, W., ... & Chen, X. (2021). Social-ecological system resilience of debris flow alluvial fans in the Awang basin, China. Journal of Environmental Management, 286, 112230. https://doi.org/10.1016/j.jenvman.2021.112230
Held, M., Imeson, A., & Montanarella, L. (2003). Economic Interests and Benefits of Sustainable Use of Soils and Land Management. Joint Res. Centre Press, Ispra, Italy.
Jolliffe, I. (2022). A 50-year personal journey through time with principal component analysis. Journal of Multivariate Analysis, 188, 104820. https://doi.org/10.1016/j.jmva.2021.104820
Loehlin, J. C. (1998). Latent Variable Models. Lawrence Erlbaum, Hillsdale, New Jersey.
Mansouri Daneshvar, M. R., Bagherzadeh, A., & Alijani, B. (2013). Application of multivariate approach in agrometeorological suitability zonation at northeast semiarid plains of Iran. Theoretical and applied climatology, 114, 139-152. https://doi.org/10.1007/s00704-012-0827-3
Manton, M.G., Angelstam, P., & Mikusinski, G. (2005). Modelling habitat suitability for deciduous forest focal species. A sensitivity analysis using different satellite land cover data. Landscape Ecology, 20: 827-839. https://doi.org/10.1007/s10980-005-3703-z
Masoumi, H., Jamali, A. A., & Khabazi, M. (2014). Investigation of role of slope, aspect and geological formations of landslide occurrence using statistical methods and GIS in some watersheds in Chahar Mahal and Bakhtiari Province. J. Appl. Environ. Biol. Sci, 4(9), 121-129.
McBoyle, G.R. (1973). Climate classification of Australia by computer. In: McBoyle GR (ed) Climate in review. HoughtonMifflin, Boston, pp 110–118
Murray, A. T. (1999). Spatial analysis using clustering methods: Evaluating central point and median approaches. Journal of Geographical Systems, 1(4), 367-383. https://doi.org/10.1007/s101090050019
Oleszczuk, P. (2007). The evaluation of sewage sludge and compost toxicity to Heterocypris incongruens in relation to inorganic and organic contaminants content. Environmental Toxicology: An International Journal, 22(6), 587-596. https://doi.org/10.1002/tox.20282
Olivas, G.U.E., Valdez, L.J.R., Aldrete, A., Gonzalez, G.M.d.J., & Vera, C.G. (2007). Suitable areas for establishing maguey cenizo plantations: definition through multicriteria analysis and GIS. Revista fitotecnia mexicana, 30: 411-419. https://doi.org/10.35196/rfm.2007.4.411
Ouyang, Y. (2005). Application of principal component and factor analysis to evaluate surface water quality monitoring network. Water Res, 39:2621-2635 https://doi.org/10.1016/j.watres.2005.04.024
Paschalidou, A. K., Kassomenos, P. A., & Bartzokas, A. (2009). A comparative study on various statistical techniques predicting ozone concentrations: implications to environmental management. Environmental monitoring and assessment, 148, 277-289. https://doi.org/10.1007/s10661-008-0158-0
Rossiter, D. G. (1996). A theoretical framework for land evaluation. Geoderma, 72(3-4), 165-190. https://doi.org/10.1016/0016-7061(96)00031-6
Russell, J. S., & Moore, A. W. (1976). Classification of climate by pattern analysis with Australasian and southern African data as an example. Agricultural Meteorology, 16(1), 45-70 https://doi.org/10.1016/0002-1571(76)90068-6.
Steiner, D. (1965). Multivariate Statistical Approach to Climatic Regionalization and Classification. EJ Brill.
Sys C, Van Ranst E, Debaveye IJ (1991) Land evaluation. Part I: Principles In Land Evaluation and Crop Production Calculations. General Administration for Development Cooperation, Agricultural publication-No. 7, Brussels-Belgium, 274
Sys, C., Van Ranst, E., Debaveye, J., & Beernaert, F. (1993). Land Evaluation. Part III: crop requirements. Agricultural Publications n° 7, GADC, Brussels, Belgium, 1993, 191 p.Wilks, D. S. (2011). Statistical methods in the atmospheric sciences (Vol. 100). Academic press.
Yussouf, N., Stensrud, D. J., & Lakshmivarahan, S. (2004). Cluster analysis of multimodel ensemble data over New England. Monthly weather review, 132(10), 2452-2462. https://doi.org/10.1175/1520-0493(2004)132%3C2452:CAOMED%3E2.0.CO;2
