Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Abstract

Artificial intelligence systems are widely accepted as a technology providing an alternative method to solve complex and ill-defined problems. Artificial neural network (ANN) is a technique with a flexible mathematical structure, which is capable of identifying a complex nonlinear relationship between the input and output data. The objective of this study was to investigate the relationship between dust concentration and wind erosion rate, and to illustrate how ANN might play an important role in the prediction of wind erosion rate. Data were recorded via field experiments by using a portable field wind tunnel. The experiments were carried out for eight different tillage applications that include the conventional, six different reduced tillage and the direct seeding practices. Particulate matter (PM) concentration generally decreased with a decrease in number or intensity of tillage operations. Direct seeding resulted in the lowest PM 10 concentration. After tillage applications, wind erosion rate varied between 113 and 1365 g m -2 h -1 . Results showed that wind erosion rate was lower in direct seeding than in conventional and reduced tillage applications. In this paper, a sophisticated intelligent model, based on a 1-(8-5)-1 ANN model with a back-propagation learning algorithm, was developed to predict the changes in the wind erosion rate due to dust concentration occurring during tillage. In addition, the prediction of the model was made according to traditional methods of wind erosion rate by using the programme Statistica, version 5. The verification of the proposed model was carried out by applying various numerical error criteria. The ANN model consistently provided better predictions compared with the nonlinear regression-based model. The relative error of the predicted values was found to be less than the acceptable limits (10%). Based on the results of this study, ANN appears to be a promising technique for predicting wind erosion rate.

Differences in the Efficiency of the Vertical Transfer of Windblown Sediment over Different Ploughed Surfaces during Wind Erosion Events

“…In the same way, Labiadh et al [27] showed that the difference in the sediment flux was about a factor 4 between agricultural surfaces tilled with a disc plough and a tiller plough and was of an order of magnitude between surfaces tilled with a disc plough and a moldboard plough. More recently, the impact of tillage practices on dust emission was also evidenced by Çarman et al [28] using a portable wind erosion tunnel in an experimental field in Turkey. Eight different tillage applications were tested: conventional tillage, six different reduced tillages, and direct seeding.…”

“…Indeed, tilled surfaces present ridges that allow the trapping of a part of the saltating particles into the furrow of the ridges, which reduces the saltation of soil grains and consequently decreases the sediment flux blowing over tilled surfaces. Thus, this decrease of the wind erosion efficiency over different tilled surfaces is dependent on the geometric characteristics of the tillage ridges, i.e., ridge height (RH) and ridge spacing (RS), e.g., [24][25][26][27][28]. As an example, Armbrust et al [25] showed that any size ridge up to 20.3 cm height on a cultivated soil is more effective in controlling wind erosion than is a smooth surface.…”

Application of Computational Intelligence in Describing Dust Emissions in Different Soil Tillage Applications in Middle Anatolia

“…The results were of approximately 2.5 mg•m −3 and 4.5 mg•m −3 for PM 10 and PM 5 , respectively, at the monitoring station found at 10 m altitude [2]. Carman et al (2016) [17] concluded that PM 10 concentration for seven different tillage applications in clay-loam soil conditions generally increased with the intensity of tillage operations. They examined the relationship between the wind erosion rate and dust concentration using artificial neural networks (ANNs) and discovered that the ANNs model consistently provided better predictions.…”

“…To evaluate the performance of the results obtained with both ANNs and polynomial models, root mean square error (RMSE), coefficient of determination (R 2 ), and relative error (ε) were utilized and calculated using the following equations [17,37]:…”

Wind Erosion Risk in Agricultural Soils under Different Tillage Systems in the Middle Anatolia

“…After each run, the sediment was collected; oven dried at 105°C, and weighed on a balance. To obtain the wind erosion rate (g m-2 h-1), the mass of the sediment (g) was divided by the test area (m2) and event duration (h) (Zamani and Mahmoodabadi, 2013;Çarman et al 2016). Measurements were made once after one day from tillage in both years.…”