Abstract
It is a challenge to find effective methods to estimate biomass over a large range of biomass values in diverse plant communities, such as typically found in mountain grasslands. We compared the performance of three non-destructive methods for estimating plant biomass (3D quadrat: a point quadrat method, plate meter: a measure of physical volume, and visual estimation: a component of the BOTANAL method) in mountain grasslands. We tested whether: (1) all methods performed equally in terms of linearity of estimations over a large range of biomass and (2) under and over-estimations of biomass were related to specific plant compositions. We estimated plant biomass in 30 plots, for which real plant biomass was measured by destructive sampling. We accounted for the significant effect of heteroscedasticity (which was significant for all three methods) when testing for the linearity of the relationship between real biomass and biomass estimates. The plate meter displayed non-linearity, being insensitive to variation of biomass at low biomass values. BOTANAL and the 3D quadrat yielded linear relationships, with BOTANAL providing better estimates of real biomass (greater R²). Specific floristic compositions (e.g. presence of Deschampsia cespitosa, Chaerophyllum sp., and abundant small forbs) explained underestimation and overestimation of biomass estimates for the plate meter and 3D quadrat while BOTANAL was insensitive to floristic composition. In heterogeneous grasslands, BOTANAL appeared to be the most appropriate method, given its linear relationship with real biomass over the whole range of biomass and its low residual variation.
This is a preview of subscription content, log in via an institution to check access.
References
Bransby DI, Matches AG, Krause GF (1977) Disk meter for rapid estimation of herbage yield in grazing trials. Agron J 69:393–396
Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New york
Calenge C (2007) Exploring habitat selection by wildlife with adehabitat. J Stat Softw 22:1–19
Catchpole WR, Wheeler CJ (1992) Estimating plant biomass—a review of techniques. Aust J Ecol 17:121–131
Coser AC, Donascimento D, Gomide JA, Dasilva JFC, Silva MDE, Garcia R, Martins CE (1991) Comparison of Botanal to other methods of pasture evaluation in natural Grasslands. Pesquisa Agropecuaria Brasileira 26:759–767
Dall’Agnol M, Scheffer-Basso SM, do Nascimento JAL, Silveira CAM, Fischer RG (2005) Elephantgrass forage yield under cold climate conditions. 2. Production and animal selectivity. Revista Brasileira De Zootecnia-Brazilian Journal of Animal Sci 34:425-432
Darmon G, Calenge C, Loison A, Julien JM, Maillard D, Lopez JF (2012) Spatial distribution and habitat selection in coexisting species of mountain ungulates. Ecography 35:44–53
Dolédec S, Chessel D (1987) Rythmes saisonniers et composantes stationnelles en milieu aquatique I- Description d’un plan d’observations complet par projection de variables. Acta Oecologica, Oecologia Generalis 8:403–426
European Community Commission (1991) EUR 12587—CORINE biotope —a method to identify and describe consistently sites of major importance for nature conservation. Office for Official Publications of European Communities, Brussels
Fehmi JS, Stevens JM (2009) A plate meter inadequately estimated herbage mass in a semi-arid grassland. Grass Forage Sci 64:322–327
Frank DA, McNaughton SJ (1990) Aboveground biomass estimation with the canopy intercept method: a plant growth form caveat. Oikos 57:57–60
Goodall DW (1952) Some considerations in the use of point quadrats for the analysis of vegetation. Aust J Scientific Res Series B-Biol Sci 5:1–41
Harmoney KR, Moore KJ, George JR, Brummer EC, Russell JR (1997) Determination of pasture biomass using four indirect methods. Agron J 89:665–672
Haydock KP, Shaw NH (1975) The comparative yield method for estimating dry matter yield of pasture. Aust J Exp Agric Animal Husb 15:663–670
Jonasson S (1988) Evaluation of the point intercept method for the estimation of plant biomass. Oikos 52:101–106
Lavorel S, Grigulis K, McIntyre S, Williams NSG, Garden D, Dorrough J, Berman S, Quetier F, Thebault A, Bonis A (2008) Assessing functional diversity in the field - methodology matters! Funct Ecol 22:134–147
Loison A, Jullien JM, Menaut P (1999) Subpopulation structure and dispersal in two populations of chamois (Rupicapra sp.). J Mamm 80:620–632
Lopez-Guerrero I, Fontenot JP, Garcia-Peniche TB (1999) Comparison of four biomass estimation methods in Tall Fescue pastures. Revista Mexicana De Ciencias Pecuarias 2:209–220
Mellors JE (1991) An evaluation of rapid visual technique for estimating seagrass biomass. Aquat Bot 42:67–73
Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692
Osborne C (1991) Statistical calibration—a review. Int Stat Rev 59:309–336
Pinheiro JC, Bates DM (2000) Mixed effects models in S and S-Plus. Springer, Berlin
Radloff L, Mucina A (2007) A quick and robust method for biomass estimation in structurally diverse vegetation. J Veg Sci 18:719–724
Rayburn EB, Lozier J (2003) A falling plate meter for estimating pasture forage mass. WVU, Morgantown, WV. Extension Fact Sheet, www.caf.wvu.edu
Said S, Pellerin M, Guillon N, Debias F, Fritz H (2005) Assessment of forage availability in ecological studies. Eur J Wildl Res 51:242–247
Schneider BH, Bell DT (1985) A simple, effective technique for rapid measurement of fuels in low shrub communities. Aust For Res 15(11):79–84
t’Mannetje L, Jones RM (2000) Field and laboratory methods for grassland and animal production research. CAB International, Wallingford
Tothill JC, Hargreaves JNG, Jones RM, McDonald CK (1992) BOTANAL—a comprehensive sampling and computing procedure for estimating pasture yield and composition. 1. Field sampling. Division of tropical crops and pastures. CSIRO, St Lucia, Queensland
White H (1980) A heteroskedasticity-consistent covariance-matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4):817–838
Acknowledgments
This project was funded by a CNRS-ATIP fund and by the ONCFS. We thank all volunteers and students that helped us in the field.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Three successive training session for visual estimate of BOTANAL (g/m2) of a same observer (C. Redjadj), using GLS regression (number of quadrat for the three training session were respectively: n = 13, 14, 20). R² are given for each training session.
Rights and permissions
About this article
Cite this article
Redjadj, C., Duparc, A., Lavorel, S. et al. Estimating herbaceous plant biomass in mountain grasslands: a comparative study using three different methods. Alp Botany 122, 57–63 (2012). https://doi.org/10.1007/s00035-012-0100-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00035-012-0100-5