Applying Multivariate Factor Analysis Approach in Land Suitability Evaluation for Medicinal- Ornamental Plant Damask Rose in Northeast of Iran

Journal of Ornamental Plants

مقاله 5، دوره 12، شماره 3، آذر 2022، صفحه 223-233   

نوع مقاله: Research Paper

نویسندگان

Alireza Anvarkhah Hakmabadi¹؛ Maryam Tatari^{* 1}؛ Ali Bagherzadeh Chaharjoui²؛ Majid Rahimizadeh³

¹Department of Agriculture, Shirvan Branch, Islamic Azad University, Shirvan, Iran

²Department of Agriculture, Mashhad Branch, Islamic Azad University, Mashhad, Iran

³Department of Agriculture, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran

چکیده

Lack of knowledge of vital factors in the production and cultivation of plants in unsuitable areas can increase the use of chemical fertilizers to prevent a reduction in plant yield. 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 damask rose plantation in North Khorasan Province, NE Iran. For this purpose, the extracted 16 variables were processed, resulting in four factors that explain about 90% of the total variance. The explained variances of those factors are varied from 28.573 to 8.855% for factors 1 and 4 after the Varimax rotation, respectively. The zonation map of land suitability revealed that 2.61% (665.6 km²) of the surface area was highly suitable, 95.78% (24410.47 km²) was moderately suitable and 1.61% (409.74 km²) of the region was marginally suitable for damask rose production. The geographical distribution revealed that the points with very high suitability are laid in the Western, middle, and Eastern parts of the study area, while the mid part of the study area and some scattered parts in the East, Northeast, and Northwest exhibited moderate suitability for damask rose. The most important limiting factors for the damask rose plantation in the study area were climatic factors including mean temperature during the growing cycle, mean temperature during the germination, and mean temperature during the flowering.

کلیدواژه‌ها

Damask rose؛ Factor analysis؛ GIS؛ Land suitability؛ Multivariate statistical

مراجع

Ahmed, B.Y.M. 1997. Climatic classification of Saudi Arabia: An application of factor-cluster analysis. Geo Journal, 41(1): 69-84.

Alijani, B., Ghohroudi, M. and Arabi, N. 2008. Developing a climate model for Iran using GIS. Theoretical and Applied Climatology, 92(1): 103-112.

Bagherzadeh, A., Gholizadeh, A. and Alizadeh Motaghi, F. 2021. The feasibility of ash and spruce forest plantations in the Northeast of Iran. Environmental Resources Research, 9(1): 1-12.

Bartzokas, A. and Metaxas, D.A. 1995. Factor analysis of some climatological elements in Athens, 1931–1992: Covariability and climatic change. Theoretical and Applied Climatology, 52(3): 195-205.

Boskabady, M.H., Shafei, M.N., Saberi, Z. and Amini, S. 2011. Pharmacological effects of Rosa damascena. Iranian Journal of Basic Medical Sciences, 14(4): 295.

Bruneau, A., Starr, J.R. and Joly, S. 2007. Phylogenetic relationships in the genus Rosa: New evidence from chloroplast DNA sequences and an appraisal of current knowledge. Systematic Botany, 32(2): 366-378.

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.

Cairns, T., Young, M., Adams, J. and Edberg, B. 2000. Modern roses XI, the world encyclopedia of roses. Academic Press, London.

Emad, M., Gheibi, F., Rasouli, S.M., Khanjanzadeh, R. and Mohammadi Jozani, S. 2012. Rosa damascena. Pooneh Publication. (In Persian)

FAO. 1976. A framework for land evaluation: Soils Bulletin 32, Food and Agriculture Organization of the United Nations, Rome, Italy.

FAO. 1984. Land evaluation for forestry, forestry paper 48: Food and Agriculture Organization of the United Nations, Rome, Italy.

Gholizadeh, A., Bagherzadeh, A. and Keshavarzi, A. 2020. Model application in evaluating land suitability for OAK and PINE forest plantations in Northeast of Iran. Geology, Ecology, and Landscapes, 4(3): 236-250.

Gorsuch, R.L. 1983. Factor analysis, 2nd ed. Lawrence Erlbaum, Hillsdale.

Harborne, J.B. 1967. The anthocyanin pigments. IV. Table of Rf value and sources. In: Comparative biochemistry of the Flavonoids. Academic Press, London.

Hongratanaworakit, T. 1994. Relaxing effect of rose oil on humans. Natural Product Communications, 2009; 4: 291-296. In: Hoover, C. “Don’t just smell the roses - eat their seed pods”. The Herbarist, 60: 51-52.

Jolliffe, I.T. 1986. Principal component analysis. Springer, New York, 271.

Kiani, M., Zamani, Z., Khalighi, A., Fatahi, R. and Byrne, D.H. 2009. Microsatellite analysis of Iranian damask rose (Rosa damascena Mill.) germplasm. Plant Breeding, 129(5): 551-557.

Lavalle, C., Micale, F., Houston, T.D., Camia, A., Hiederer, R., Lazar, C., Conte, C., Amatulli, G. and Genovese, G. 2009. Climate change in Europe. 3. Impact on agriculture and forestry. A review. Agronomy for Sustainable Development, 29(3): 433-446.

Loehlin, J.C. 1992. Latent variable models. Lawrence Erlbaum, Hillsdale.

Mansouri Daneshvar, M.R., Bagherzadeh, A. and Alijani, B. 2013. Application of multivariate approach in agrometeorological suitability zonation at Northeast semiarid plains of Iran. Theoretical and Applied Climatology, 114(1): 139-152.

Marcelis-van Acker, C.A.M. 1995. Effect of temperature on development and growth potential of axillary buds in roses. Scientia Horticulturae, 63(3-4): 241-250.

McBoyle, G.R. 1973. Climate classification of Australia by computer. In: McBoyle GR (ed) climate in review. Houghton Mifflin, 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.

Ouyang, Y. 2005. Application of principal component and factor analysis to evaluate surface water quality monitoring network. Water Research, 39: 2621–2635.

Paschalidou, A.K., Kassomenos, P.A. and Bartzokas, A. 2009. A comparative study on various statistical techniques predicting ozone concentrations: Implications to environmental management. Environmental Monitoring and Assessment, 148(1): 277-289.

Russell, J.S. and 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.

Steiner, D. 1965. A multivariate statistical approach to climatic regionalization and classification. Tijdschr Hon Ned Aardr Genoot, 82: 329–347.

Sys, C., van Ranst, E. and Debaveye, J. 1991. Land evaluation. Part I: Principles in land evaluation and crop production calculations. Agricultural Publications N. 7, GADC, Brussels, Belgium, 1991.

Sys, C., van Ranst, E., Debaveye, J. and Beernaert, F. 1993. Land evaluation. Part III: Crop requirements. Agricultural Publications n° 7, GADC, Brussels, Belgium, 1993, 191 p.

Rossiter, D.G. 1996. A theoretical framework for land evaluation. Geoderma, 72(3–4):165–190.

Ushio, A., Mae, T. and Makino, A. 2008. Effects of temperature on photosynthesis and plant growth in the assimilation shoots of a rose. Soil Science and Plant Nutrition, 54(2): 253-258.

Wilks, D.S. 2011. Statistical methods in the atmospheric sciences. An introduction. International Geophysics Series 59. Academic, San Diego, pp 467.

Yadollah, A., Shahhosseini, R., Sahms, A. and Khosravi, H. 2012. Sustainable agriculture in Quran. Journal of Agricultural Science and Sustainable Production, 22 (2): 101-111.

Yussouf, N., Stensrud, D.J. and Lakshmivarahan, S. 2004. Cluster analysis of multi-model ensemble data over new England. Monthly Weather Review, 132(10): 2452-2462.