Does liming grasslands increase biomass productivity without causing detrimental impacts on net greenhouse gas emissions?

Soil acidification has negative impacts on grass biomass production and the potential of grasslands to mitigate greenhouse gas (GHG) emissions. Through a global review of research on liming of grasslands, the objective of this paper was to assess the impacts of liming on soil pH, grass biomass production and total net GHG exchange (nitrous oxide (N2O), methane (CH4) and net carbon dioxide (CO2)). We collected 57 studies carried out at 88 sites and covering different countries and climatic zones. All of the studies examined showed that liming either reduced or had no effects on the emissions of two potent greenhouse gases (N2O and CH4). Though liming of grasslands can increase net CO2 emissions, the impact on total net GHG emission is minimal due to the higher global warming potential, over a 100-year period, of N2O and CH4 compared to that of CO2. Liming grassland delivers many potential advantages, which justify its wider adoption. It significantly ameliorates soil acidity, increases grass productivity, reduces fertiliser requirement and increases species richness. To realise the maximum benefit of liming grassland, we suggest that acidic soils should be moderately limed within the context of specific climates, soils and management.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.