Soil properties as key predictors of global grassland production: Have we overlooked micronutrients?

Abstract

Fertilisation experiments have demonstrated that nutrient availability is a key determinant of biomass production and carbon sequestration in grasslands. However, the influence of nutrients in explaining spatial variation in grassland biomass production has rarely been assessed. Using a global dataset comprising 72 sites on six continents, we investigated which of 16 soil factors that shape nutrient availability associate most strongly with variation in grassland aboveground biomass. Climate and N deposition were also considered. Based on theory‐driven structural equation modelling, we found that soil micronutrients (particularly Zn and Fe) were important predictors of biomass and, together with soil physicochemical properties and C:N, they explained more unique variation (32%) than climate and N deposition (24%). However, the association between micronutrients and biomass was absent in grasslands limited by NP. These results highlight soil properties as key predictors of global grassland biomass production and point to serial co‐limitation by NP and micronutrients.

Only zinc (Zn) among micronutrients is an important predictor of grassland production

“…To further demonstrate this, we used these seven variables to construct an SEM. This SEM was similar to Radujković et al (2021) final SEM but without composite variables, which showed that Fe and SOM had no significant direct effects on Biomass (Figure 1B). After removing these two paths from the model, we found that the SEM was significantly improved, with a lower AICc value (Figure 1C).…”

“…After removing these two paths from the model, we found that the SEM was significantly improved, with a lower AICc value (Figure 1C). These results indicate that the final SEM of Radujković et al (2021) is not the best model to explain the variation in grassland production. Finally, through model comparison and optimization (the R code is provided in the Supplementary Material), we obtained the best SEM to understand the key factors determining grassland production (Figure 1D).…”

“…In the final model of Radujković et al (2021) (Figure 1A), seven environmental variables were used to explain the variation in Biomass. We first used these seven variables to fit a multiple linear model: lm (Biomass ∼ MAPgs + Ndep + CEC + SOM + C:N + Fe + Zn).…”

“…using glmulti (Calcagno and De Mazancourt, 2010) based on AICc to determine the best combination of all environmental variables initially used in Radujković et al (2021). This method allowed us to perform model selection by creating a set of models with all possible combinations of initial variables and sorting them according to the AICc.…”

“…The effects of climatic factors and soil nutrients in the determination of plant biomass production have long been of central interest to ecologists (Huxman et al, 2004;Fay et al, 2015). Recently, Radujković et al (2021) found that soil micronutrients (particularly Zn and Fe) were important predictors of aboveground biomass production (Biomass). In their analyses, the combination of atmospheric factors that best explained the variation in Biomass was selected first; then, soil physicochemical properties, C-N-P nutrients, and other nutrients (K, Ca, Mg, Na, S, Zn, Fe, B, Cu, and Mn) were sequentially added to construct structural equation models (SEMs).…”