Soil, water and nutrient losses by interrill erosion from green cane cultivation

Interrill erosion occurs by the particle breakdown caused by raindrop impact, by particle transport in surface runoff, by dragging and suspension of particles disaggregated from the soil surface, thus removing organic matter and nutrients that are essential for agricultural production. Crop residues on the soil surface modify the characteristics of the runoff generated by rainfall and the consequent particle breakdown and sediment transport resulting from erosion. The objective of this study was to determine the minimum amount of mulch that must be maintained on the soil surface of a sugarcane plantation to reduce the soil, water and nutrient losses by decreasing interrill erosion. The study was conducted in Pradópolis, São Paulo State, in 0.5 x 1.0 m plots of an Oxisol, testing five treatments in four replications. The application rates were based on the crop residue production of the area of 1.4 kg m-2 (T1- no cane trash; T2-25 % of the cane trash; T3- 50 % trash; T4-75 % trash; T5-100 % sugarcane residues on the surface), and simulated rainfall was applied at an intensity of 65 mm h-1 for 60 min. Runoff samples were collected in plastic containers and soon after taken to the laboratory to quantify the losses of soil, water and nutrients. To minimize soil loss by interrill erosion, 75 % of the cane mulch must be maintained on the soil, to control water loss 50 % must be maintained and 25 % trash controls organic matter and nutrient losses. This information can contribute to optimize the use of this resource for soil conservation on the one hand and the production of clean energy in sugar and alcohol industries on the other.

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

Soil temperature regimes in Finland

Selostus: Maan lämpötila Suomessa