Spatio-temporal decision uncertainty of selected soil physical parameters can enhance variable rate irrigation

Authors

  • Louis Angura Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen
  • Dhimas Sigit Bimantara University of Debrecen
  • Tamás Magyar Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen
  • Erika Buday Bódi Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen
  • Zsolt Zoltán Fehér Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen

DOI:

https://doi.org/10.18380/SZIE.COLUM.2024.11.1.05

Keywords:

Spatio-temporal decision uncertainty, Sequential Gaussian simulation, cost-effective irrigation

Abstract

The effectiveness of crop irrigation via sprinkler systems could be improved by environmental variability inherent to field conditions, thus leading to the sub-optimal irrigation of certain sections. To rectify this discrepancy, in-situ soil characteristics were methodically correlated with the region's hydrological circumstances and root zone, assigning a distinct level of uncertainty to each decision point. A stochastic geodatabase was then generated, offering prospective applications in precision agriculture. The experimental agricultural field in Nyírbátor, Hungary, served as the reference point, with the constraints posed by variability being surmounted through a dual-layer iteration of random sampling structures employing the sequential Gaussian simulation (SGS) method. For this purpose, 25 physical, 9 chemical, and 11 soil microelements were examined from samples extracted from 105 boreholes in an 85-hectare cornfield while adopting a regular sampling scheme within a 100 × 100 m grid. Each soil parameter estimation underwent the following process: 1. Organization of data and application of exploratory statistics for outlier identification; 2. Normal score transformation; 3. Exploratory variography; 4. Sequential Gaussian simulations, leading to the construction of a series of plausible, equally probable realizations; 5. Computation of medians and the 95% confidence intervals. These methodologies were deployed concerning the soil characteristics, with porosity being selected as the representative soil parameter for the Nyírbátor cornfield. Porosity was our focus physical parameter because the micro and macro soil structures greatly influence the hydraulic characteristics of the soil such as water infiltration, hydraulic conductivity and moisture retention. Comparative assessments of the Hydrus 3D hydrological models of kriged and sequential Gaussian simulation surfaces were conducted. Results highlighted the efficacy of sequential Gaussian simulation in encapsulating the field's heterogeneity, and the accompanying uncertainty served as a decision-making tool in the diversified water application across the field. The results were validated using field data observations of soil moisture in the corn field from 2020 and 2021 respectively and nonetheless, the uncertainty divergence between the Hydrus outputs unveiled the knowledge deficit concerning actual spatial patterns of soil porosity. The established workflow offers a cost-efficient dynamic methodology for water resource management, potentially curtailing overall irrigation expenditure by variably applying water to parcels based on uncertainty estimates.

Author Biographies

  • Tamás Magyar, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen

    Dr. Tamás Magyar
    magyar.tamas@agr.unideb.hu

  • Erika Buday Bódi, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen

    Dr. Erika Buday Bódi
    bodi.erika@agr.unideb.hu

  • Zsolt Zoltán Fehér, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen

    Dr. Zsolt Zoltán Fehér
    feher.zsolt@agr.unideb.hu

References

Bi, S., Beer, M., Cogan, S., & Mottershead, J. (2023). Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial. Mechanical Systems and Signal Processing 204(-), 110784. http://doi.org/10.1016/j.ymssp.2023.110784

Borges Júnior, J. C. F., & Andrade, C. d. L. T. d. (2021). Two-dimensional spatial distribution modeling of sprinkler irrigation. Revista Ceres 68(4), 257-266. http://doi.org/10.1590/0034-737x2021680400

Bwambale, E., Abagale, F. K., & Anornu, G. K. (2023). Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling. Smart Agricultural Technology 5(-), 100251. http://doi.org/10.1016/j.atech.2023.100251

Deutsch, C. V., & Journel, A. G. (1992). Geostatistical software library and user’s guide (Vol. 8) (No. (91)). New York: Oxford University Press.

Faechner, T., Pyrcz, M., & Deutsch, C. (2000). Soil remediation decision making in presence of uncertainty in crop yield response. Geoderma 97(1), 21-38. http://doi.org/10.1016/S0016-7061(00)00024- 0

Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.

Hua, L., Jiang, Y., Li, H., & Qin, L. (2022). Effects of Different Nozzle Orifice Shapes on Water Droplet Characteristics for Sprinkler Irrigation. Horticulturae 8(6), 538. http://doi.org/10.3390/horticulturae8060538

Indoria, A. K., Sharma, K. L., Reddy, K. S., & Rao, C. S. (2017). Role of soil physical prop- erties in soil health management and crop productivity in rainfed systems-I: Soil physical constraints and scope. Current Science 112(12), 2405-2414.

Kabolizadeh, M., Karimian, R., Rangzan, K., Alizadeh, B., & Maroufi, K. (2023). Development of a geodatabase for retrieval of geochemical data from oil wells: A case study from the Gachsaran oilfield; SW Iran. Geoenergy Science and Engineering 225(1), 211621. http://doi.org/10.1016/j.geoen.2023.211621

Karandish, F., & Šimu ̊nek, J. (2019). A comparison of the HYDRUS (2D/3D) and SALTMED models to investigate the influence of various water-saving irrigation strategies on the maize water footprint. Agricultural Water Management 213(1), 809-820. http://doi.org/10.1016/j.agwat.2018.11.023

Li, X., Zhao, W., Li, J., & Li, Y. (2018). Crop Yield and Water Use Efficiency as Affected by Different Soil-Based Management Methods for Variable-Rate Irrigation in a Semi-Humid Climate. Transactions of the ASABE 61(6), 1915-1922. http://doi.org/10.13031/trans.13036

Magyar, T., Fehér, Z., Buday-Bódi, E., Tamás, J., & Nagy, A. (2023). Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation. Computers and Electronics in Agriculture 213(-), 108159. http://doi.org/10.1016/j.compag.2023.108159

Nyengere, J., Okamoto, Y., Funakawa, S., & Shinjo, H. (2023). Analysis of spatial heterogeneity of soil physicochemical properties in northern Malawi. Geoderma Regional 35(-), e00733. http://doi.org/10.1016/j.geodrs.2023.e00733

Quigley, M. Y., Rivers, M. L., & Kravchenko, A. N. (2018). Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices. Frontiers in Environmental Science 6(-), . http://doi.org/10.3389/fenvs.2018.00028

Rossi, R. E., Borth, P. W., & Tollefson, J. J. (1993). Stochastic simulation for characterizing ecological spatial patterns and appraising risk. Ecological Applications 3(4), 719-735. http://doi.org/10.2307/1942103

Soulis, K. X. (2013). Development of a simplified grid cells ordering method facilitating GIS-based spatially distributed hydrological modeling. Computers & Geosciences 54(-), 160-163. http://doi.org/10.1016/j.cageo.2012.12.003

Sui, R., Fisher, D. K., & Reddy, K. N. (2015). Yield response to variable rate irrigation in corn. Journal of Agricultural Science 7(11), 11. http://doi.org/10.5539/jas.v7n11p11

Sui, R., & Vories, E. D. (2020). Comparison of Sensor-Based and Weather-Based Irrigation Scheduling. Applied Engineering in Agriculture 36(3), 375-386. http://doi.org/10.13031/aea.13678

Sun, H., Slaughter, D., Ruiz, M. P., Gliever, C., Upadhyaya, S., & Smith, R. (2010). RTK GPS mapping of transplanted row crops. Computers and Electronics in Agriculture 71(1), 32-37. http://doi.org/10.1016/j.compag.2009.11.006

Tamás, J., Nagy, A., Gálya, B., Riczu, P., & Jóvér, J. (2018). Summary Report on the Development of Water and Energy-Efficient Fertilization Technology in Arable Farming in a Digital Precision Environment - Development of the Foundations for New Irrigation Technology and Participation in On-Site Adaptation (Tech. Rep.).

Wang, J.-P., Liu, T.-H., Wang, S.-H., Luan, J.-Y., & Dadda, A. (2023). Investigation of porosity variation on water retention behaviour of unsaturated granular media by using pore scale Micro-CT and lattice Boltzmann method. Journal of Hydrology 626(-), 130161. http://doi.org/10.1016/j.jhydrol.2023.130161

Zhao, W., Li, J., Yang, R., & Li, Y. (2018). Determining placement criteria of moisture sensors through temporal stability analysis of soil water contents for a variable rate irrigation system. Precision Agriculture 19(4), 648-665. http://doi.org/10.1007/s11119-017-9545-2

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Published

2024-07-12

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How to Cite

Spatio-temporal decision uncertainty of selected soil physical parameters can enhance variable rate irrigation. (2024). COLUMELLA – Journal of Agricultural and Environmental Sciences, 11(1), 5-17. https://doi.org/10.18380/SZIE.COLUM.2024.11.1.05

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