Experimental and Modeling Evaluation of Impacts of Different Tillage Practices on Fitting Parameters of Kostiakov’s Cumulative Infiltration Empirical Equation

Abstract

The evaluation and modeling of the water infiltration rate into the soil are important to all aspects of water resources management and the design of irrigation systems for agricultural purposes. However, research focused on experimental studies of infiltration rates in clay soils under different tillage practices remains minimal. Therefore, an empirical prediction model for cumulative water infiltration needs to be created to estimate water depth under different tillage practices. Thus, the present research investigated the impacts of different tillage practices, including plow type (three tillage systems: moldboard, disk, and rotary plows), tillage depth (100 and 200 mm) and four soil compactions levels (0, 1, 3, and 5 tractor wheel passes), on cumulative infiltration behavior in a clay soil under a randomized complete design with three replications. Double-ring infiltration experiments were conducted to collect infiltration data. The research was conducted in three different stages. The first stage was performed through a field test to obtain infiltration data, the second stage involved using a Kostiakov empirical equation (Z = q × tb) for cumulative infiltration to acquire the fitting parameters of “q” and “b”, and in the last stage, we predicted the fitting parameters of “q” and “b” based on soil mean weight diameter, tillage depth, and four soil compaction levels by applying regression data mining approaches in Weka 3.8 software. The results show that the effects of relevant factors on the cumulative water infiltration depth of the soil could be statistically significant (p < 0.05). The Kostiakov model, with an average coefficient of determination of 0.939, had a good fitting effect on the cumulative water infiltration depth process of the investigated soil. The average, lowest, and maximum values of the “q” parameter were 2.7073, 2.2724, and 3.1277 mm/minb, respectively, while for the “b” parameter, they were 0.5523, 0.5424, and 0.5647, respectively. Furthermore, the evaluation of several regression data mining approaches determined that the KStar (K*) data mining approach, with a root mean square error of 0.0228 mm/minb, a mean absolute error of 0.0179 mm/minb, and a correlation coefficient of 0.997, was the most accurate method for fitting parameter “q” using the testing dataset. The most accurate method for fitting the parameter “b” estimation was determined to be the Multilayer Perceptron method, with a root mean square error of 0.0026, a mean absolute error of 0.0013, and a correlation coefficient of 0.962, using the testing dataset. Therefore, this research, which consisted of in situ field observation experiments and infiltration modeling of the infiltration process in a clay soil, provides an essential theoretical basis for improving models of the rate of cumulative infiltration. Moreover, the proposed methodology could be employed for simulation of the fitting parameters “q” and “b” for soil water cumulative infiltration processes, not only for irrigation management purposes under regular crop production conditions, but also for the selection of the most suitable tillage practices to modify the soil during the agriculture season to conserve water and prevent yield declines. The results support the understanding of the infiltration processes in a clay soil and demonstrate that tillage practices could reduce the water infiltration rate into the soil.