Modelling of permanent wilting point from routine soil properties on a typical alfisol

Authors

  • Affinnih Kehinde University of Ilorin, Faculty of Agriculture, Department of Agronomy, PMB 1515 Ilorin, Kwara State Nigeria. https://orcid.org/0000-0002-3535-5835
  • Olaniyan John University of Ilorin, Faculty of Agriculture, Department of Agronomy, PMB 1515 Ilorin, Kwara State Nigeria.
  • Ahamefule Henry University of Ilorin, Faculty of Agriculture, Department of Agronomy, PMB 1515 Ilorin, Kwara State Nigeria.
  • Anwanane Nnenne University of Ilorin, Faculty of Agriculture, Department of Agronomy, PMB 1515 Ilorin, Kwara State Nigeria.

DOI:

https://doi.org/10.21704/pja.v8i1.2047

Keywords:

Pedotransfer function, regression models, soil moisture content, toposequence, water coefficient

Abstract

Soil water holding capacity at permanent wilting point is imperative for plant water stress in specific soil type. This study was undertaken to formulate a regression model or equation for predicting permanent wilting points (PWP) of soils on a typical Alfisol of basement complex origin at the Teaching and Research Farm of the University of Ilorin. A total of forty five (45) disturbed and forty five (45) undisturbed soils samples were collected along a toposequence (upper, middle and bottom slope) at 3 depths: 0 cm – 30 cm, 30 cm – 60 cm, and 60 cm – 90 cm. Soil properties of the disturbed and undisturbed samples were determined using basic experimental methods and/ or calculated using reputable techniques. The measured soil properties include the proportions of soil separates, bulk density, total porosity, PWP and organic matter. Three different models were developed for predicting PWP of soil using regression model technique. There was no significant relationship between PWP and soil separates, bulk density and total porosity. However, only the silt content was positively correlated with PWP (r=0.22; p<0.05). Although, model three of PWP with the highest adjusted coefficient of determination (0.2952) emerged as the optimal choice. The model clarifies 30 % of part of variance in the mean square error of PWP with sand, silt and clay contributing statistically to the model. This implies that additional variables and techniques such as spatial and machine learning aside those used in the present study would provide a more reliable pedotransfer function for predicting PWP in the soil. 

 

Downloads

Download data is not yet available.

References

Amsili, J. P., Van Es, H. M., & Schindelbeck, R. R. (2022). An available water capacity pedotransfer using random forest -2020 Cornell soil health model: Harvard data verse https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/U5DAEP.

Amsili, J. P., Van Es, H. M. & Schindelbeck, R. R. (2024). Pedotransfer functions for field capacity, permanent wilting point and available water capacity based on random forest models for routine soil health analysis. Communication in soil science and plant analysis, 55(13), 1–18. https://doi.org/10.1080/00103624.2024.2336573.

Barnett, V., & Lewis T. (1994). Outliers in Statistical Data. 3rd edition. Wiley & Sons, New York, XVII, 582p.

Blake, G. R., & Hartge, K. H. (1986). “Bulk density”, In: A. Klute (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, part 1. (pp. 363–375) 2nd edition.

Bouma, J. (1989). Using Soil Survey Data for Quantitative Land Evaluation. In: Stewart, B. A. (eds.), Advances in Soil Science, 9. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3532-3_4.

Brady C. N., & Weil, R. R. (1999). “The nature and properties of soil”, 12th edition, Prentice Hall Publishers, London.

Donatelli, M., Acutis M., & Laruccia N. (1996). Pedotransfer functions: Evaluation of methods to estimate soil water content at field capacity and wilting point.

Gee, G.W, & Or, D. (2002). Particle size analysis. In: Dane, J. H., Toop, G. C. (Eds.), Methods of Soil Analysis Part 4, (pp 255–293). SSSA. Book Series No.5. Madison, Wi, USA. Soil Sci. Society of Am.

John, J. A., & P. Prescott (1975). Critical values of a test to detect outliers in factorial experiments. Journal of the Royal Statistical Society Series C: Applied Statistics, 24(1), 56–59.

Kinoshita, R., Moebius-Clane, B. N., Van Es, H. M., Hively, W. D., & Bilgillis, A. V. (2012). Strategies for soil quality assessment using visible and near-infrared reflectance spectroscopy in a western Kenya chronosequence. Soil Science Society of America Journal, 76(5), 1776–1788. https://doi.org/10.2136/sssaj2011.0307.

Kolo, E., Takim, F. O., & Fadayomi, O. (2012). Influence of planting date and weed Management practice on weed emergence, growth and yield of maize (Zea mays L.) in Southern Guinea Savanna of Nigeria. African crop science journal, 9(4), 615–627.

Kukal, M. S., Irmak, S., Dobos, R., & Gupta, S. (2023). Atmospheric dryness impacts on crop yields are buffered in soils in the higher available water capacity. Geoderma 429, 116270. https://doi.org/10.1016/j.geoderma.2022.116270.

Libohora, Z., Seybold, C., WWysocki, D., Wills, S., Schoeneberger, P., Williams, C., Lindbo, D. Stott, D. & Owens, P. R. (2018). Reevaluating the effects of soil organic matter and other properties on available water-holding capacity using the National Cooperative Soil Survey Characterization Database. Journal of Soil and Water Conservation, 73(4), 411–421 https://doi.org/10.2489/jswc.73.4.411.

Martens, H., & Naes, T. (1984). Multivariate Calibration. In: Kowalski, B.R. (eds.), Chemometrics. NATO ASI Series. Springer. Dordrecht. https://doi.org/10.1007/978-94-017-1026-8_5.

Mbagwu, J., Lal, R., & Scott, T. W. (1983). Physical properties of three soils in southern Nigeria. Soil Science, 136(1), 48–55.

Minasny, B., Mcbratney, A. B., & Bristow, K. L. (1999). Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma, 93(3–4), 225–253. https://doi.org/10.1016/S0016-7061(99)00061-0.

Minasny, B., & Mcbratney, A. B. (2018). Limited effect of organic matter on soil available water capacity. European journal of soil science, 69(1), 39–47. https://doi.org/10.1111/ejss.12475.

Morrison, D. F. (1976) Multivariate Statistical Methods. New York, McGraw Hill.

Myers, R. H., & D. C. Montgomery (1995). Response Surface Methodology: Process and Product Optimization using Designed Experiments. John Willey and Sons, New York INC.

Nelson, D. W., & Sommers, L. E. (1982). Total carbon, organic carbon and organic matter. In: Page, A. L., Miller, R. H., and Keeney, D. R. (eds) Methods of Soil Analysis: Chemical and Microbiological Properties. Part 2, 2nd edition. Madison, WI: ASA-SSSA.

Obi, C. I., Obi, J. C., & Onweremadu, E. U. (2012). Modeling of Permanent Wilting from Particle Size Fractions of Coastal Plain Sands Soils in Southeastern Nigeria. International Scholarly Research Notices, 2012, 1-5.

Odu, C. T. I., Babalola, O., Udo, E. J., Ogunkunle, A. O., Bakare, T. A., & Adeoye, G. O. (1986). Laboratory Manual for Agronomic Studies in Soil, Plant and Microbiology. University of Ibadan.

Ogban, P. I., & Ekerette, I. O. (2001). Physical and chemical properties of the coastal plain sands soils of southeastern Nigeria. Nigerian Journal of Soil and Environmental Research, 2, 6–14.

Olaniyan, J. O. (2003). An Evaluation of the Soil Map of Nigeria for Land use Planning in Kwara State [Doctoral dissertation, University of Arizona]. University of Ibadan, Nigeria.

Olorede, K. O., Mohammed, I. A., & Adeleke, B. L. (2013). Economic Selection of Efficient Level of NPK 16:16:16 Fertilizer for Improved Yield Performance of a Maize Variety in the South Guinea Savannah Zone of Nigeria. Mathematical Theory and Modeling, 3(1), 27–39.

Olorede, K. O., & Mudasiru, A. A. (2013). Economic Analysis and Modelling of Effects of NPK

Fertilizer Levels on Yield of Yam. Mathematical Theory and Modeling, 3(1), 108–118.

Phillips, J. D. (2007). Development of texture contrast soils by a combination of bioturbation and translocation. Catena, 70(1), 92–104. https://doi.org/10.1016/j.catena.2006.08.002

R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

Saxton, K. E., & Rawls, W. J. (2006). Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil science society of America journal, 70(5), 1569–78. https://doi.org/10/2136/ssaj2005.0117

Souza, Z. M., Junior, J. M., & Pereira, G. T. (2009). Spatial variability of the physical and mineralogical properties of the soil from the areas with variation in landscape shapes. Brazilian Archives of Biology and Technology, 52(2). 305–316.

Stefansky, W. (1972). Rejecting Outliers in Factorial Designs. Technometrics, 14(2), 469–479.

Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minanasy, B., Mishra, U., Nemes, A., Pachepsky, Y. A., Padarian, J., & Vereecken, H. (2017). Pedotransfer functions in earth system science: challenges and perspectives. Reviews of geophysics, 55(4), 1199–256. https://doi.org/1.1002/207RG000581

Vereecken, H., Maes, J., Feyen, J., & Darius, P. (1989). Estimating the soil moisture retention characteristic from texture, bulk density, and carbon content. Soil Sci., 148(6), 389–403. https://doi.org/10.1097/00010694-198912000-00001

Wosten, J. H. M., Lilly, A., Nemes, A., & Le Bas, C. (1999). Development and use of a database of hydraulic properties of European soils. Geoderma, 90,169–185. https://doi.org/10.1016/S0016-7061(98)00132-3

Downloads

Published

2024-04-30

How to Cite

Affinnih, K., Olaniyan, hn, Ahamefule, H., & Anwanane, N. (2024). Modelling of permanent wilting point from routine soil properties on a typical alfisol. Peruvian Journal of Agronomy, 8(1), 55-68. https://doi.org/10.21704/pja.v8i1.2047