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

Paper Details

Research Paper 01/05/2014
Views (542)
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

Agroforestry in woody-encroached Sub-Saharan savannas: Transforming ecological challenges into sustainable opportunities

Yao Anicet Gervais Kouamé, Pabo Quévin Oula, Kouamé Fulgence Koffi, Ollo Sib, Adama Bakayoko, Karidia Traoré, J. Biodiv. & Environ. Sci. 27(3), 10-22, September 2025.

Extreme rainfall variability and trends in the district of Ouedeme, municipality of Glazoue (Benin)

Koumassi Dègla Hervé, J. Biodiv. & Environ. Sci. 27(3), 1-9, September 2025.

Heterosis breeding, general and specific combining ability and stability studies in pearl millet: Current trends

Ram Avtar, Krishan Pal, Kavita Rani, Rohit Kumar Tiwari, Mahendra Kumar Yadav, J. Biodiv. & Environ. Sci. 27(2), 117-124, August 2025.

Combining ability, heterosis and stability for yield and fibre quality traits in cotton: Breeding approaches and future prospects

Rohit Kumar Tiwari, Krishan Pal, R. P. Saharan, Ram Avtar, Mahendra Kumar Yadav, J. Biodiv. & Environ. Sci. 27(2), 109-116, August 2025.

Bridging the COPD awareness gap in marginalized populations: Findings from a multicentre study in Khalilabad, Sant Kabir Nagar, Uttar Pradesh, India

Anupam Pati Tripathi, Jigyasa Pandey, Sakshi Singh, Smita Pathak, Dinesh Chaudhary, Alfiya Mashii, Farheen Fatima, J. Biodiv. & Environ. Sci. 27(2), 97-108, August 2025.

Antioxidant and anti-inflammatory activity of Pleurotus citrinopileatus Singer and Pleurotus sajor-caju (Fr.) Singer

P. Maheswari, P. Madhanraj, V. Ambikapathy, P. Prakash, A. Panneerselvam, J. Biodiv. & Environ. Sci. 27(2), 90-96, August 2025.

Mangrove abundance, diversity, and productivity in effluent-rich estuarine portion of Butuanon River, Mandaue City, Cebu

John Michael B. Genterolizo, Miguelito A. Ruelan, Laarlyn N. Abalos, Kathleen Kay M. Buendia, J. Biodiv. & Environ. Sci. 27(2), 77-89, August 2025.

Cytogenetic and pathological investigations in maize × teosinte hybrids: Chromosome behaviour, spore identification, and inheritance of maydis leaf blight resistance

Krishan Pal, Ravi Kishan Soni, Devraj, Rohit Kumar Tiwari, Ram Avtar, J. Biodiv. & Environ. Sci. 27(2), 70-76, August 2025.