Predicting root biomass for semiarid grassland species of the southern Chihuahuan Desert

Abstract

Most of carbon in grasslands comes from underground biomass, particularly in arid grassland ecosystems. However, estimation of root biomass in these ecosystems has been poorly studied. In this study was analyzed the correlation between above ground plant variables and root biomass to develop statistical models for reliable root biomass estimations. Twenty-six plant species were collected within grazing-excluded grasslands. Linear, exponential and logarithmic regression models were performed for each species and for the whole data set to determine the variables that best predicted root biomass. Only Frankenia gypsophila and Dalea gypsophila showed root/shoot ratio (RSR) higher than one. Enneapogon desvauxii and Atriplex acantocarpha had a RSR close to one. Eight species showed statistical significance in at least one of the correlation analyses but only Tiquilia canescens, Bouteloua gracilis, Machaerantera pinnatifida, Lesquerella fendleri, and Atriplex acanthocarpa had both statistical significance and acceptable coefficient of determination (r2 ≥0.50). Using the Marquardt exponential method, 14 out of 15 studied species showed a high determination coefficient and statistical significance. This method was adequate (r2=0.853) to estimate root biomass for the whole set of plants from plant height and crown diameter.

Keywords

• Plant biomass
• arid ecosystems
• allometric equations
• Marquardt method
• regression model
• northeastern Mexico