Estimating the biomass production of three rangeland species using geo-statistic techniques, Taleghan, Iran

Paper Details

Research Paper 01/05/2014
Views (643)
current_issue_feature_image
publication_file

Estimating the biomass production of three rangeland species using geo-statistic techniques, Taleghan, Iran

Fateme noori, Bahram gholinejad
J. Biodiv. & Environ. Sci. 4(5), 252-257, May 2014.
Copyright Statement: Copyright 2014; The Author(s).
License: CC BY-NC 4.0

Abstract

The study area of the current study is located in Taleghan region, Iran;enclosing about 54 hectares. What is argued here, is estimating the amount of biomass production of some rangeland species by making use of geo-statistical techniques. Random systematic sampling design was applied with 100 quadrats of one square meter area in two phases. In the first phase, random starting point located in the Phlomis-Astragalus,25 quadrates were drawn parallel to the slope and another 25 quadrates perpendicular to the slope, keeping regular 10-meter distances in between. In the second phase also, another 50 quadrates were drawn. For each quadrat, biomass of the species and GPS locations were recorded (discarding the quadrates lacking the species of interest). The corresponding variogram for the 100 quadrates was plotted in the next step and showed a low level of homogeneity for the recorded biomasses. Using the OrdinaryKriging and by analyzing the obtained variogram, the amount of biomass of Astragalusgossipinu, BromustomentellusandAgropyronsibiricumwas determined for the quadrates delimiting one square meter. In the obtained variogram, the random variance was high implying a poor representation of the biomass production for the species. Accordingly, the geo-statistic techniques based on analyzing variograms and by applying Kriging method are not the appropriate way to perform such studies.

Akhavan R, Zobairi M, ZahediAmiri Gh, Namiranian M, Mandallaz D, 2006. Spatial structure and estimation of forest growing stock using geostatistical approach in the Caspian region of Iran, Iranian Journal of Natural Resources 59 (1), 89-102 (In Persian).

Carroll SS, Pearson D L. 2000. Detecting and modeling spatial and temporal dependence in conservation biology. Journal of Conservation Biology 14, 1893–1897.

Conan GY, Maynou F, Sarda F. 1992. Direct assessment of the harvestable biomass from a stock of Nephropsnorvegicus, seasonal and spatial variations. ICES Conference Meetings K, 22.

Jost A. 1993. Geostatistischeanalyse des StichprobenfehlerssystematischerStichproben, Ph.D. thesis, Unuversity of Freiburg in Berisgau, 90.

Goovaerts P. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York.

Gunnarsson F, Holm S, Holmgren P, Thuresson T. 1998. On the potential of kriging for forest management planning. Scan. Journal of Forest Research 13, 237-245. 10.1080/02827589809382981

Maravelias CD, Reid DG, Simmonds EJ, Haralabous J. 1996. Spatial analysis and mapping of acoustic survey data in the presence of high local variability: geostatistical application to North Sea herring (Clupeaharengus). Canadian Journal of Fisheries and Aquatic Sciences 53, 1497–1505. 10.1139/f96-079

Moghadam MR. 2001. Descriptive and statically ecology of vegetation, University of Tehran, 285.

Rossi RE, Mulla DJ, Franz EH. 1992. Geostatistical tools for modeling and interpreting ecological data spatial dependence. Ecological Monographs 62, 277–314. http://dx.doi.org/10.2307/2937096

Sokal RR, Oden NL. 1978. Spatial autocorrelations in biology. 1. Methodology. Biolojical Journal of Linnean Society 10, 199–228.

Zimmrman DL, Zimmrman MB. 1991. A comparison of spatial semivariogram estimators and corresponding ordinary kriging predictors. – Technometrics 33, 77–92. 10.1111/j.1095-8312.1978.tb00013.x

Related Articles

Using chitosan made from modified chitosan (Crab shells) for dye adsorption, equilibrium, kinetic, and response surface methods

M. Priyanga, V. Gomathi Priya, P. Bhuvaneswari, T. Shanmuga Vadivu, S. Viswanathan, G. Annadurai, R. Soranam*, J. Biodiv. & Environ. Sci. 28(2), 85-98, February 2026.

Effects of logging regimes on woody species diversity and stand structure in community forests adjacent to the Dja biosphere reserve, Cameroon

Nanga Charnelle Prudence*, Angoni Hyacinthe, Menyene Etoundi Laurent Florent, Ifo Averti Suspense, Nkemnkeng Francoline Jong, Mbolo Marie Marguerite, J. Biodiv. & Environ. Sci. 28(2), 76-84, February 2026.

Analysis of soil physicochemical characteristics and heavy metal concentrations in Lourdes, Alubijid, Misamis Oriental

Prosibeth G. Bacarrisas*, Romeo M. del Rosario, Angelo Mark P. Walag, J. Biodiv. & Environ. Sci. 28(2), 49-58, February 2026.

Tick-borne blood parasites in small ruminants: An epidemiological study of Anaplasma sp. and Babesia sp. in Cagayan, Philippines

Kathlyn B. Cruz*, Jhaysel G. Rumbaoa, Mary Ann M. Santos, Bryan Jerome R. Bassig, John Michael U. Tabil, J. Biodiv. & Environ. Sci. 28(2), 34-48, February 2026.

Diversity, spatial and seasonal distribution of gastropod molluscs in Taï national park (Côte d’Ivoire): Influence of environmental factors

Doue Obin*, Memel Jean-Didié, Kouadio Behegbin Habib Herbert, J. Biodiv. & Environ. Sci. 28(2), 20-33, February 2026.

Assessment of heavy metal levels in spring water of Dansolihon, Cagayan de Oro City

Faith M. Guimary*, Romeo M. Del Rosario, Angelo Mark P. Walag, J. Biodiv. & Environ. Sci. 28(2), 12-19, February 2026.

Evaluating curriculum alignment, accuracy, and readability of ‘environmental disaster, sanitation, and waste management

Analyn I. Diola*, Priscilla R. Castro, J. Biodiv. & Environ. Sci. 28(2), 1-11, February 2026.