Predicting the spatio-temporal dynamic of soil surface characteristics after tillage

• Published in: Soil & tillage research 2011-08, Vol.114 (2), p.135-145
• Main Authors: Pare, N., Andrieux, P., Louchart, X., Biarnes, A., Voltz, M.
• Format: Article
• Language: English
• Subjects: Agricultural sciences; Agronomy. Soil science and plant productions; Biological and medical sciences; Catchment; Catchments; Cropping systems. Cultivation. Soil tillage; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Infiltration; Life Sciences; Logistic regression; Runoff; Soil crusting; Soil science; Soil study; Soil tillage; Tillage. Tending. Growth control; Vineyard
• ISSN: 0167-1987; 1879-3444
• DOI: 10.1016/j.still.2011.04.003

► Soil surface characteristics evolution can be well predicted with a logistic regression approach. ► Rainfall appears to be the main predicting factor of soil reconsolidation. ► During the last part of reconsolidation, stoniness accelerates the crusting process. Soil surface characteristics (SSC) influence strongly hydrological processes and are known to vary largely in space and time according to soil characteristics and soil management. Because tillage is a main source of variation, the goal of this study was to present and evaluate a prediction model of the temporal variation of the SSC after tillage at the catchment level. The study focused on bare soils prevailing in spring and summer. A logistic regression approach was used to predict the evolution along three stages, starting from the fresh tillage stage to the crusted soil stage. This method provides the probabilities of occurrence of each stage. The predictor candidates tested were a rainfall characteristic, namely cumulative rainfall depth or cumulative kinetic energy, basic soil properties and tillage features. The results showed that a model based on cumulative kinetic energy since tillage and soil stoniness accurately predicts the dynamics of SSC: the rate of well classified SSC was 91%. However, no significant difference in the prediction performance was found using as predictor either cumulative kinetic energy or cumulative rainfall amount since tillage. In the prediction model, the rainfall characteristic was the most significant predictor for the SSC evolution and the only one during the first stages of crust development since tillage. Stoniness was also shown to influence SSC evolution but only during the last stages of crust development: high stone cover speeds up soil surface evolution. The same approach using logistic regression can be applied elsewhere but will require a re-examination of the most relevant predicting variables. Finally, to be able to predict the soil surface characteristic evolution on an annual scale, weed growth characteristics must be considered in the list of predictor candidates.