The Dynamics of Ground Consolidation Through Soil Penetration Resistance and Artificial Neural Networks
Keywords:
Intensive Farming, Soil penetration, Soil Properties, Organic CarbonAbstract
Ground consolidation, a consequence of intensive farming practices, poses challenges to soil health and mechanical properties. Assessing Ground consolidation is vital for effective tillage and machinery selection in agriculture. Soil Penetration Resistance (SPR) serves as a key indicator, impacting water uptake, root growth, and overall crop yields. This study explores the intricate relationship between SPR and various soil properties, including moisture, bulk density, texture, and organic carbon. Laboratory determination of SPR is expensive and time-consuming, leading to efforts to predict SPR using indirect methods and mathematical models based on easily accessible soil properties. The study employs Artificial Neural Networks (ANN) to predict SPR under diverse conditions, considering the impact of tractor speed and soil moisture. The research aims to enhance understanding, refine prediction models, and contribute to sustainable agricultural practices. The methodology involves comprehensive field experiments, soil sampling, and advanced modeling techniques. The results demonstrate the significance of soil texture and moisture in SPR predictions, emphasizing the potential of ANN models for accurate assessments. The study contributes valuable insights into Ground consolidation, supporting improved land management and environmental sustainability in agriculture.
References
R. P. de Lima, A. R. da Silva, and Á. P. da Silva, “soilphysics: An R package for simulation of soil compaction induced by agricultural field traffic,” Soil Tillage Res., vol. 206, Feb. 2021, doi: 10.1016/J.STILL.2020.104824.
R. P. de Lima, A. R. da Silva, A. P. da Silva, T. P. Leão, and M. R. Mosaddeghi, “Soilphysics: An R package for calculating soil water availability to plants by different soil physical indices,” Comput. Electron. Agric., vol. 120, pp. 63–71, Jan. 2016, doi: 10.1016/J.COMPAG.2015.11.003.
G. ZHENG et al., “Pedotransfer functions for predicting bulk density of coastal soils in East China,” Pedosphere, vol. 33, no. 6, pp. 849–856, Dec. 2023, doi: 10.1016/J.PEDSPH.2023.01.014.
G. H. Huang, R. D. Zhang, and Q. Z. Huang, “Modeling soil water retention curve with a fractal method,” Pedosphere, vol. 16, no. 2, pp. 137–146, 2006, doi: 10.1016/S1002-0160(06)60036-2.
H. Bayat, S. Asghari, M. Rastgou, and G. R. Sheykhzadeh, “Estimating Proctor parameters in agricultural soils in the Ardabil plain of Iran using support vector machines, artificial neural networks and regression methods,” Catena, vol. 189, Jun. 2020, doi: 10.1016/J.CATENA.2020.104467.
A. P. da Silva and B. D. Kay, “Estimating the Least Limiting Water Range of Soils from Properties and Management,” Soil Sci. Soc. Am. J., vol. 61, no. 3, pp. 877–883, May 1997, doi: 10.2136/SSSAJ1997.03615995006100030023X.
J. de Dios Herrero, J. C. Colazo, D. Buschiazzo, and J. Galantini, “Influence of sand gradation on compaction of loess soils,” Soil Tillage Res., vol. 196, Feb. 2020, doi: 10.1016/J.STILL.2019.104414.
J. Massah, K. Asefpour Vakilian, and S. Torktaz, “Supervised Machine Learning Algorithms Can Predict Penetration Resistance in Mineral-fertilized Soils,” Commun. Soil Sci. Plant Anal., vol. 50, no. 17, pp. 2169–2177, Sep. 2019, doi: 10.1080/00103624.2019.1654505.
C. Seker et al., “Identification of regional soil quality factors and indicators: A case study on an alluvial plain (central Turkey),” Solid Earth, vol. 8, no. 3, pp. 583–595, May 2017, doi: 10.5194/SE-8-583-2017.
“View of Conversion of Fertile Agricultural Land into Built-Up by Estimation of Pixel Based Land Surface Temperature (LST).” Accessed: Feb. 22, 2024. [Online]. Available: https://journal.50sea.com/index.php/IJASD/article/view/469/957
M. D. Fredlund, G. W. Wilson, and D. G. Fredlund, “Use of the grain-size distribution for estimation of the soil-water characteristic curve,” Can. Geotech. J., vol. 39, no. 5, pp. 1103–1117, 2002, doi: 10.1139/T02-049.
A. R. Dexter, “Soil physical quality: Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth,” Geoderma, vol. 120, no. 3–4, pp. 201–214, Jun. 2004, doi: 10.1016/J.GEODERMA.2003.09.004.
M. M. H. Fernandes et al., “Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks,” Catena, vol. 189, Jun. 2020, doi: 10.1016/J.CATENA.2020.104505.
J. L. Hernanz, H. Peixoto, C. Cerisola, and V. Sánchez-Girón, “An empirical model to predict soil bulk density profiles in field conditions using penetration resistance, moisture content and soil depth,” J. Terramechanics, vol. 37, no. 4, pp. 167–184, 2000, doi: 10.1016/S0022-4898(99)00020-8.
H. Kashi, S. Emamgholizadeh, and H. Ghorbani, “Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression, ANN (RBF, MLP), and ANFIS Models,” Commun. Soil Sci. Plant Anal., vol. 45, no. 9, pp. 1195–1213, 2014, doi: 10.1080/00103624.2013.874029.
J. W. Hummel, I. S. Ahmad, S. C. Newman, K. A. Sudduth, and S. T. Drummond, “Simultaneous soil moisture and cone index measurement,” Trans. Am. Soc. Agric. Eng., vol. 47, no. 3, pp. 607–618, May 2004, doi: 10.13031/2013.16090.
H. Wang, L. Wang, X. Huang, W. Gao, and T. Ren, “An empirical model for estimating soil penetrometer resistance from relative bulk density, matric potential, and depth,” Soil Tillage Res., vol. 208, Apr. 2021, doi: 10.1016/J.STILL.2020.104904.
“Agriculture | Free Full-Text | Using Models and Artificial Neural Networks to Predict Soil Compaction Based on Textural Properties of Soils under Agriculture.” Accessed: Feb. 17, 2024. [Online]. Available: https://www.mdpi.com/2077-0472/14/1/47
W. R. Whalley, J. To, B. D. Kay, and A. P. Whitmore, “Prediction of the penetrometer resistance of soils with models with few parameters,” Geoderma, vol. 137, no. 3–4, pp. 370–377, Jan. 2007, doi: 10.1016/J.GEODERMA.2006.08.029.
C. M. P. Vaz, J. M. Manieri, I. C. de Maria, and M. Tuller, “Modeling and correction of soil penetration resistance for varying soil water content,” Geoderma, vol. 166, no. 1, pp. 92–101, Oct. 2011, doi: 10.1016/J.GEODERMA.2011.07.016.
M. Tian, S. Qin, W. R. Whalley, H. Zhou, T. Ren, and W. Gao, “Changes of soil structure under different tillage management assessed by bulk density, penetrometer resistance, water retention curve, least limiting water range and X-ray computed tomography,” Soil Tillage Res., vol. 221, Jul. 2022, doi: 10.1016/J.STILL.2022.105420.
A. R. Barzegar, M. A. Asoodar, and M. Ansari, “Effectiveness of sugarcane residue incorporation at different water contents and the proctor compaction loads in reducing soil compactibility,” Soil Tillage Res., vol. 57, no. 3, pp. 167–172, 2000, doi: 10.1016/S0167-1987(00)00158-6.
