Comparing the effects of hydromulching and application of biodegradable plastics on surface runoff and soil erosion in deforested and burned lands

Abstract Several techniques, such as hydromulching (HM) and addition of organic residues (such as biodegradable plastics, BP) to soil have been proposed for conservation of soil affected by deforestation and wildfire. However, there is the need to support the task of land managers for the adoption of the most effective soil conservation technique, considering that the impacts on soil properties and hydrology are different due to the different mechanisms (mainly based on root actions for hydromulching and on supply of organic matter for application of bioplastics residues). This study comparatively evaluates the hydrological and erosive effects of HM, addition of BP residues to soil, and lack of any treatments (control) at the plot scale and under simulated rainfall in deforested and burned forestlands of Northern Iran. These effects have been associated to changes in key properties of soil and root characteristics due to the treatments, using multivariate statistical analysis. Moreover, regression models have been setup to predict surface runoff and soil erosion for both treatments. HM was more effective (–65% of runoff and –61% in soil loss) than application of BP (–22% and –19%, respectively) in controlling the soil’s hydrological and erosive response, the latter being extremely high in control plots (over 6 tons/ha). These reductions were closely associated to significant increases in organic matter and aggregate stability of soil, to a decrease in bulk density after the treatments, and to the grass root growth, which further improved soil hydrology after HM. The Principal Component Analysis provided a synthetic parameter measuring the soil response to rainfall and treatments. The cluster analysis discriminated the three soil conditions (HM, application of BP and control), according to the changes in soil properties and root growth in HM, in as many groups of soil samples. The multiple regression analysis provided two linear models that predict surface runoff and soil loss with a very high accuracy (r 2 > 0.98) for a precipitation with given depth and intensity.