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Research Article

Pedotransfer Functions for Field Capacity, Permanent Wilting Point, and Available Water Capacity Based on Random Forest Models for Routine Soil Health Analysis

ORCID Icon, ORCID Icon &
Pages 1967-1984 | Received 21 Aug 2023, Accepted 26 Mar 2024, Published online: 03 Apr 2024

References

  • Amer, A-M. M., S. D. Logsdon, and D. Davis. 2009. Prediction of hydraulic conductivity as related to pore size distribution in unsaturated soils. Soil Science 174 (9):508–15. doi:10.1097/SS.0b013e3181b76c29.
  • Amsili, J. P., H. M. Van Es, D. M. Aller, and R. R. Schindelbeck. 2023. Empirical approach for developing production environment soil health benchmarks. Geoderma Regional 34:e00672. doi:10.1016/j.geodrs.2023.e00672.
  • Amsili, J. P., H. M. Van Es, and R. R. Schindelbeck. 2021. Cropping system and soil texture shape soil health outcomes and scoring functions. Soil Security 4:100012. doi:10.1016/j.soisec.2021.100012.
  • Amsili, J. P., H. M. Van Es, and R. R. Schindelbeck. 2022. An available water capacity pedotransfer function using random forest - 2020 cornell soil health model: Harvard Dataverse. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/U5DAEP.
  • Andrews, S. S., D. L. Karlen, and C. A. Cambardella. 2004. The soil management assessment framework. Soil Science Society of America Journal 68 (6):1945–62. doi:10.2136/sssaj2004.1945.
  • Araya, S. N., and T. A. Ghezzehei. 2019. Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations. Water Resources Research 55 (7):5715–37. doi:10.1029/2018WR024357.
  • Bagnall, D. K., C. L. S. Morgan, M. Cope, G. M. Bean, S. Cappellazzi, K. Greub, and C. W. Honeycutt, C. L. Norris, E. Rieke, P. Tracy. 2022. Carbon-sensitive pedotransfer functions for plant available water. Soil Science Society of America Journal 86 (3):612–29. doi:10.1002/saj2.20395.
  • Bauer, A., and A. L. Black. 1992. Organic carbon effects on available water capacity of three soil textural groups. Soil Science Society of America Journal 56 (1):248–54. doi:10.2136/sssaj1992.03615995005600010038x.
  • Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Carranza, C., C. Nolet, M. Pezij, and M. Van Der Ploeg. 2021. Root zone soil moisture estimation with random forest. Journal of Hydrology 593:125840. doi:10.1016/j.jhydrol.2020.125840.
  • Christy, I., A. Moore, D. Myrold, and M. Kleber. 2023. A mechanistic inquiry into the applicability of permanganate oxidizable carbon (poxc) as a soil health indicator. Soil Science Society of America Journal 87 (5):1083–95. doi:10.1002/saj2.20569.
  • Chu, C.-H., and L. J. Johnson. 1985. Relationship between exchangeable and total magnesium in pennsylvania soils. Clays and Clay Minerals 33 (4):340–44. doi:10.1346/CCMN.1985.0330410.
  • Cornell Soil Health Laboratory. 2022. Sample collection. https://soilhealthlab.cals.cornell.edu/testing-services/soil-sample-collection/.
  • Fan, Y., H. Li, and G. Miguez-Macho. 2013. Global patterns of groundwater table depth. Science 339 (6122):940–43. doi:10.1126/science.1229881.
  • Greenwell, B. M. 2017. Pdp: An r package for constructing partial dependence plots. The R Journal 9 (1):421–36. doi:10.32614/RJ-2017-016.
  • Gupta, S. C., and W. E. Larson. 1979. Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density. Water Resources Research 15 (6):1633–35. doi:10.1029/WR015i006p01633.
  • Idowu, O. J., H. M. Van Es, G. S. Abawi, D. W. Wolfe, R. R. Schindelbeck, B. N. Moebius-Clune, and B. K. Gugino. 2009. Use of an integrative soil health test for evaluation of soil management impacts. Renewable Agriculture and Food Systems 24 (3):214–24. doi:10.1017/S1742170509990068.
  • Kettler, T. A., J. W. Doran, and T. L. Gilbert. 2001. Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Science Society of America Journal 65 (3):849–52. doi:10.2136/sssaj2001.653849x.
  • Kinoshita, R., B. N. Moebius-Clune, H. M. Van Es, W. D. Hively, and A. V. Bilgilis. 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):1776–88. doi:10.2136/sssaj2011.0307.
  • Kukal, M. S., S. Irmak, R. Dobos, and S. Gupta. 2023. Atmospheric dryness impacts on crop yields are buffered in soils with higher available water capacity. Geoderma 429:116270. doi:10.1016/j.geoderma.2022.116270.
  • Liaw, A., and M. Weiner. 2022. Randomforest: Breiman and cutler’s random forests for classification and regression. R package version 4.7-1.1. https://cran.r-project.org/web/packages/randomForest/index.html.
  • Liaw, A., and M. Wiener. 2002. Classification and regression by randomforest. R News 2 (3):18–22.
  • Libohova, Z., C. Seybold, D. Wysocki, S. Wills, P. Schoeneberger, C. Williams, and P. R. Owens, D. Stott, P. R. Owens. 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):411–21. doi:10.2489/jswc.73.4.411.
  • Mayer, M., and A. Stando. 2023. Shapviz: Shap visualizations. R package version 0.8.0. https://github.com/ModelOriented/shapviz.
  • Mayer, M., D. Watson, and B. Prezemyslaw. 2023. Kernalshap: Kernal shap. R package version 0.3.7. https://github.com/ModelOriented/kernelshap.
  • Minasny, B., and A. B. Mcbratney. 2018. Limited effect of organic matter on soil available water capacity. European Journal of Soil Science 69 (1):39–47. doi:10.1111/ejss.12475.
  • Moebius-Clune, B. N., D. J. Moebius-Clune, B. K. Gugino, O. J. Idowu, R. R. Schindelbeck, A. J. Ristow, and G. S. Abawi. 2017. Comprehensive Assessment of Soil Health - the Cornell framework. Edition 3.2. Cornell University. Geneva, NY. http://soilhealth.cals.cornell.edu/training-manual/
  • Moebius, B. N., H. M. Van Es, R. R. Schindelbeck, O. J. Idowu, D. J. Clune, and J. E. Thies. 2007. Evaluation of laboratory-measured soil properties as indicators of soil physical quality. Soil Science 172 (11):895–912. doi:10.1097/ss.0b013e318154b520.
  • Molnar, C. 2023. Interpretable Machine Learning - a Guide for Making Black Box Models interpretable: Leanpub. https://christophm.github.io/interpretable-ml-book/
  • Navidi, M. N., J. Seyedmohammadi, and S. A. Seyed Jalali. 2022. Predicting soil water content using support vector machines improved by meta-heuristic algorithms and remotely sensed data. Geomechanics and Geoengineering 17 (3):712–26. doi:10.1080/17486025.2020.1864032.
  • Nemes, A., Y. A. Pachepsky, and D. J. Timlin. 2011. Toward improving global estimates of field soil water capacity. Soil Science Society of America Journal 75 (3):807–12. doi:10.2136/sssaj2010.0251.
  • Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties. Soil Science Society of America Journal 70 (2):327–36. doi:10.2136/sssaj2005.0128.
  • Nemes, A., R. T. Roberts, W. J. Rawls, Y. A. Pachepsky, and M. T. Van Genuchten. 2008. Software to estimate −33 and −1500kpa soil water retention using the non-parametric k-nearest neighbor technique. Environmental Modelling & Software 23 (2):254–55. doi:10.1016/j.envsoft.2007.05.018.
  • Norris, C. E., G. M. Bean, S. B. Cappellazzi, M. Cope, K. L. H. Greub, D. Liptzin, and C. W. Honeycutt, P. W. Tracy, C. L. S. Morgan, C. W. Honeycutt. 2020. Introducing the north american project to evaluate soil health measurements. Agronomy Journal 112 (4):3195–215. doi:10.1002/agj2.20234.
  • Nunes, M. R., H. M. Van Es, R. Schindelbeck, A. J. Ristow, and M. R. Ryan. 2018. No-till and cropping system diversification improve soil health and crop yield. Geoderma 328:30–43. doi:10.1016/j.geoderma.2018.04.031.
  • Padarian, J., B. Minasny, and A. B. Mcbratney. 2020. Machine learning and soil sciences: A review aided by machine learning tools. SOIL 6 (1):35–52. doi:10.5194/soil-6-35-2020.
  • Palusynska, A., P. Biecek, and Y. Jiang. 2022. Randomforestexplainer: Explaining and visualizing random forests in terms of variable. R package version 0.10.1. https://github.com/ModelOriented/randomForestExplainer/tree/v0.10.1.
  • Ramcharan, A., T. Hengl, D. Beaudette, and S. Wills. 2017. A soil bulk density pedotransfer function based on machine learning: A case study with the ncss soil characterization database. Soil Science Society of America Journal 81 (6):1279–87. doi:10.2136/sssaj2016.12.0421.
  • Rawls, W. J., D. L. Brakensiek, and K. E. Saxton. 1982. Estimation of soil water properties. Transactions of the ASAE 25 (5):1316–20. doi:10.13031/2013.33720.
  • Rawls, W. J., Y. A. Pachepsky, J. C. Ritchie, T. M. Sobecki, and H. Bloodworth. 2003. Effect of soil organic carbon on soil water retention. Geoderma 116 (1):61–76. doi:10.1016/S0016-7061(03)00094-6.
  • R Core Team. 2022. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Reynolds, W. D., and G. C. Topp. 2008a. Chapter 69: Soil water analyses: Principles and parameters. In Soil sampling and methods of analysis, ed. M. R., Carter and E. G. Gregorich, 913–37. 2nd ed. Boca Raton, FL: CRC Press.
  • Reynolds, W. D., and G. C. Topp. 2008b. Chapter 72: Soil water desorption and imbibition: Tension and pressure techniques. In Soil sampling and methods of analysis, ed. M. R. Carter and E. G. Gregorich. 2nd ed. 981–97. Boca Raton, FL: CRC Press.
  • Rivera, J. I., and C. A. Bonilla. 2020. Predicting soil aggregate stability using readily available soil properties and machine learning techniques. CATENA 187:104408. doi:10.1016/j.catena.2019.104408.
  • Robertson, B. B., P. C. Almond, S. T. Carrick, V. Penny, H. W. Chau, and C. M. S. Smith. 2021. Variation in matric potential at field capacity in stony soils of fluvial and alluvial fans. Geoderma 392:114978. doi:10.1016/j.geoderma.2021.114978.
  • Robinson, D. 2017. The impressive growth of r. The Overflow. https://stackoverflow.blog/2017/10/10/impressive-growth-r/.
  • Ros, G. H., S. E. Verweij, S. J. C. Janssen, J. De Haan, and Y. Fujita. 2022. An open soil health assessment framework facilitating sustainable soil management. Environmental Science & Technology 56 (23):17375–84. doi:10.1021/acs.est.2c04516.
  • Rubio, V., J. P. Amsili, D. G. Rossiter, A. J. Mcdonald, and H. M. Van Es. 2024. Mapping soil health at regional scale: Disentangling drivers and predicting spatial land use effects. Geoderma. In Press.
  • Sanderman, J., K. Savage, and S. R. S. Dangal. 2020. Mid-infrared spectroscopy for prediction of soil health indicators in the united states. Soil Science Society of America Journal 84 (1):251–61. doi:10.1002/saj2.20009.
  • Saxton, K. E., and W. J. Rawls. 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal 70 (5):1569–78. doi:10.2136/sssaj2005.0117.
  • Schaap, M. G., F. J. Leij, and M. T. Van Genuchten. 2001. Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251 (3):163–76. doi:10.1016/S0022-1694(01)00466-8.
  • Schindelbeck, R. R., B. N. Moebiues-Clune, D. J. Moebius-Clune, K. S. Kurtz, M. Ozturk, B. Binkerd-Dale, and H. M. Van Es. 2022. Cornell university comprehensive assessment of soil health laboratory standard operating procedures. Cornell University. Ithaca, NY. https://soilhealthlab.cals.cornell.edu/resources/standard-operating-procedures-sops/.
  • Shiri, J., A. Keshavarzi, O. Kisi, and S. Karimi. 2017. Using soil easily measured parameters for estimating soil water capacity: Soft computing approaches. Computers and Electronics in Agriculture 141:327–39. doi:10.1016/j.compag.2017.08.012.
  • Storer, D. A. 1984. A simple high sample volume ashing procedure for determination of soil organic matter. Communications in Soil Science and Plant Analysis 15 (7):759–72. doi:10.1080/00103628409367515.
  • Tóth, B., M. Weynants, A. Nemes, A. Makó, G. Bilas, and G. Tóth. 2015. New generation of hydraulic pedotransfer functions for europe. European Journal of Soil Science 66 (1):226–38. doi:10.1111/ejss.12192.
  • Tseng, G. 2018. Interpreting complex models with shap values. https://gabrieltseng.github.io/posts/2018-06-20-SHAP/.
  • Van Looy, K., J. Bouma, M. Herbst, J. Koestel, B. Minasny, U. Mishra, and H. Vereecken, A. Nemes, Y. A. Pachepsky, J. Padarian, M. G. Schaap. 2017. Pedotransfer functions in earth system science: Challenges and perspectives. Reviews of Geophysics 55 (4):1199–256. doi:10.1002/2017RG000581.
  • Ward, R. 2021. Ward guide. Kearney, NE: Ward Laboratories. https://www.wardlab.com/resources/ward-guide/.
  • Weil, R. R., K. R. Islam, M. A. Stine, J. B. Gruver, and S. E. Samson-Liebig. 2003. Estimating active carbon for soil quality assessment: A simplified method for laboratory and field use. American Journal of Alternative Agriculture 18 (1):3–17. doi:10.1079/AJAA2003003.
  • Wolf, A., and D. Beegle. 1995. Recommended soil tests for macronutrients: Phosphorous, potassium, calcium and magnesium. In Recommended soil testing procedures for the northeastern united states, ed. J. Sims and A. Wolf, 30–38. Newark, Delaware: Agricultural Experiment Station University of Delaware.
  • Wösten, J. H. M., Y. A. Pachepsky, and W. J. Rawls. 2001. Pedotransfer functions: Bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology 251 (3):123–50. doi:10.1016/S0022-1694(01)00464-4.
  • Zhang, Y., and M. G. Schaap. 2017. Weighted recalibration of the rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (rosetta3). Journal of Hydrology 547:39–53. doi:10.1016/j.jhydrol.2017.01.004.
  • Zhang, Y., and M. G. Schaap. 2019. Estimation of saturated hydraulic conductivity with pedotransfer functions: A review. Journal of Hydrology 575:1011–30. doi:10.1016/j.jhydrol.2019.05.058.