| Summary: | One of the biggest problems in the estimation of aboveground biomass at the global level is the choice of a correct allometric model. In Mexico,there is a need to quantify the biomass of species in arid zones. For this reason, the objectives of this work were to adjust allometric equationsfor estimating biomass of Prosopis using a bayesian approach (EB) and to quantify the error in the adjustment of the models: EB, Ordinary LeastSquares (OLS) and one get from a research published in 2012. The Bayesian model was developed on the basis of probability distributions of parameters(a and b) a priori, collected from seven experimentation sites in which we estimated the biomass (B) through basal diameter (Db) usingpower equations. We compared the approaches in five sizes of samples (TM) (10, 30, 60, 90 and 120); in each of them, 1000 repetitions withoutreplacement were carried out. The 144 trees measured in the sampling sites were used to validate the setting for each sub-sample. The resultsshowed that the EB presented the lowest error variability in the different TM. The MCO adjusted similar to EB, however its variability and thepresence of outliers grew to decrease TM. The adjustment with the parameters of that research made in 2012 presented the greatest variabilityand demonstrated the high degree of uncertainty when estimating the biomass with fixed parameters. It is recommended the application of EBfor the estimation of biomass in other species of interest and their application in national inventories.
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